and it is always the other way around that is how you put it on okay i think the the most of the bad words are at the beginning when the people try to put in on at this precise time yeah so you start i think right okay so we all attended interspeech no yeah so we are going to talk about the papers from interspeech so how many sessions you attended huh this is not a good question i think whoo i mean did you attend any any session all of them of course all of them of course you can not attend multiple session at the same time so yeah that is really that is really your so any general impression or feedback 0 yeah that is true ah i cannot put it okay bad answer right so f for any of you it is a 1st conference or maybe for 0 no no not at all no i mean in the sense that it is interspeech is really big conference for speech so did you attend before like yeah i it is not every year right well geneva that is why i am here at idiap because i attended geneva eurospeech ok ok yeah 0 ok yeah so then you are hiding a you have attended before yes yeah we met in also in i c s l p right yeah i see see the 1st time so yeah yeah yeah in yeah so how how did you like the conference anyway i t i when in compared to to th the others the previous one i think it is quite interesting but only annoying thing is this multiple sessions and most sometimes you can not able to go to oral talk this oral presentations most of the time i c but yeah why why you cannot because because like posters is you can spend a lot of time posters looking at m many posters than sitting 20 minutes for one oral presentation yeah so in t 20 minutes you can see at least 23 posters and you can directly talk to people so exactly yeah meet even i found like very few people in some oral presentations right i think most of the people are like their own posters only okay so you attended mostly posters i guess so how many presentation you you but yeah yeah how presentations are hardly handful of like f 5 or more yeah i liked the invited speaker about the implant in the ear yeah 0 yeah ah ok it is quite good no he was from australia right the guy yeah i think so yeah yeah he is very famous i think for that right yeah he is the the inventor of that implant and for for the ear i think ah okay yeah even some panel discussions on that human speech recognition reducing gap between the a s r and and h s r it was a bit interesting lot of arguments and yeah b differently it is mainly the differences different approaches of engineers versus linguists or phoneticians and yeah s yeah i realized it were that there were panel discussions only at the place where i could see the the t t you know there were some boards saying that there is some panel discussion ah okay but i i could not get no one knew where is it what it is where it happens and why and so forth yeah even the panel discussions w i think one is really held in small room so people were really crowded it was com yeah finally i found but well this is not this speech is not restricted only to papers and these things you know so how did you like lisbon then no quite good yeah it is a bit hot still but the bea the beach nearby is quite nice yeah yeah yeah you should try it i do not know did you go around yeah w we went to the costa caparica there with the bus before or after yeah okay we we were we were supposed to take take the direct one but we did not so you went to a beach yeah cause i to a like atlantic sea coast or yeah yeah i stayed i stayed 2 days before so i went you ju you just take the train maybe that is the same i am not sure huh train north it is not so far from lisbon half an hour half an hour train yeah yeah north yeah west or north i do not know northwest okay so you stayed at the at the same coast not not you did not go to the island cross the river where where is the bridge and and then okay so that was a different place no no no not very far no no ah ok we went to like far south to lagos and yeah that wa ha yeah it is different yeah yeah yeah with hamed hemant and these people yeah that was really good those beaches are really good yeah the more south to th the hole through the water yeah even dolphin in the yeah but the weather was really hot the south it is more than 35 yeah but i think lisbon those days are pretty good like seem to me quite not cold but still okay like reasonable lisbon was good little bit bit yeah even local transport it is it is 0 yeah but yeah most of the time the buses are really crowded yeah i was happy to be back in suisse after few few days it is better to be here i think huh yeah yeah i was yeah because it is really big city no yeah it is big city many people and no like i was very surprised that even the trams did have a a c there yeah yeah i could not see it at at all those new only right not the old one but yeah yeah r right but can you imagine something like in yeah ah it is not necessary to have it maybe it is a c well some time yes but not so many days or it it was much better if if if you would have to spend a hour and a half that hemant and the others spent while going with a ca with a bus to to the conference center from from the the hotel that we were yeah 0 yeah yeah that is true conference there was a direct bus and it took them hour and a half then yeah yeah no re yeah but that is that is not good direct bus is not good like well y so you can go to the direct ce central place and then occas yeah yeah yeah it is we can take the tram and a train better to ta it is it is all conditioned yeah yeah that was the right choice but to take something which which was direct in quotes yeah the easiest way yeah da ah it was n not the f direct as anything else but what is the place the the central i mean where we change the bus to yeah in the yeah maybe you can check the booklet ah sorry i do not remember the name is it there no no no it is or s i am not sure yeah i it is supposed to be there it sounds sounds like yeah it is there like i used to find bus numbers from book only yeah 0 you have got the s yeah originally i brought it because i marked some papers but yeah let us go on this was just a onl only 01 which surprised me a bit it was speaker recognition i think but it does not really matter they were trying to model covariance of different components for g m m instead of using a diagonal of what 0 okay but then if you use a full it is too much yeah so they use some approach where they tie them automatically and after that well 0 okay pruning of covariance matrix it is like in it is more like tying it is still full but different components are tied different components of the matrix are tied but what what does it mean tied then huh equal like yeah but then you have to store the whole matrix and plus some extra information what is tied to what or b no no you have to store a minimum number a reduced number of parameters no i don right and they are tied in a linear way actually sorry not equal but it does not yeah but it is like all the semi tied covariance matrices or yeah yeah exactly that is it so they ju yeah i do not know about anything like this but so did they reduce it is in between the direct covariance this is full covariance yeah should try to reduce some parameters by tying interesting thing is that they applied it on speaker recognition and on the features before they had 3 different ways to decorrelate the features tying 0 and and this they showed it without this yeah with diagonal g m m or with this and with this with semi tied covariance matrices it was a less much less sensitive to which decorrelation procedure you choose ah okay if y so so they obtain better results with that finally or a a also better but of course it depends on the number of of parameters you put yeah but right in your tie in but if you compare no if you compare it with some baseline let us say you use just g m m with diagonal covariance matrices yeah yeah no i think they got good results with just using diagonals or definitely yeah yeah i i think i think it was even slightly better yeah a and no decorrelation before like yeah maybe i can find it maybe decorrelation again they do d c t or k l t or they do that yeah or l d a is there or but ooh the the main point is to to like make it faster the the the decoding or what l d a yeah l d a l d a or like make it possible to use a full covariance matrix yeah but but there must be the sense right because once you decorrelate the data then you do not need to have full i know yeah well that is possible why why it should be i impossible so no but still there is there is still ev that is why like people come yeah that is so that is that means that the decorrelation is not optimal right so so that is the reason yeah definitely it is not optimum so it is it is also like doing along along the diagonals all again so it is completely impossible to do it with full covariance matrix why it is really computationally really yeah so see suppose if 39 in te 39 to 39 for every yeah yeah so yeah computationally e expensive but yeah yeah it is yeah of course from the point of view of computation but otherwise it is there is no problem no there is a thing no there there is not with okay yeah ne no problem but if you see so many models and so many mixtures yeah yeah okay so so here you have to choose either many many things to store and huge computation right but yeah 30 you can see you c you can not really like even you can not the models itself is like 38 times more than that so right right so y if you have like 5 megabytes or 10 megabytes of models then yeah yeah yeah no depends yeah yeah but even i think it is really bit like impossible for the really big systems i guess like storing storing itself yeah it is this one so yeah yeah definitely it is i improved covariance modeling it is almost impossible for like l v c aside impossible to use it right yeah yeah yeah yeah it is really ti you have got dimension 39 times 39 yeah yeah for every you are so you are to store all this 30 yeah so yeah yeah so there are no results there it is impossible to use it and you cannot even train it right no no but in the proceedings you can find them more or less i think yeah yeah they are better yeah they tried p c a l d a m l l t so from 0 so that is interesting huh i i it is not really anything new but the they just applied that to speaker i d speak yeah different task and yeah yeah yeah so i found one paper interesting i think you know vivek most of you know this is vivek is paper on like variable scale feature extraction yeah normally we use to fix it window for feature extraction like 25 milliseconds or 30 seconds here he is proposing variable scale because the fixed scale is non optimal for it is non optimum because like for vowels you can have m much longer much longer and for plosives and these things you really shorter like even around less than 20 milliseconds also shorter yeah okay so he is ki proposing like this variable scale window f kind of online for each segment why it has to be online yeah you can do like you can measure and you can do but he is basically doing some likelihood ratio testing okay so if so the main idea is like suppose you have one segment so he is trying to find the stationality cautious stationality of that segment right like extended as as more as much as possible to keep the okay yeah as much as possible like but yeah definitely you have to assume some l like minimum and maximum sizes of your own of course of course so he is using minimum of minimum is i think twe i do not 125 milliseconds i think wh what is the minimum and maximum maximum is 60 milliseconds so it is not so i is not it smaller the minimum no so the minimum 25 milliseconds whoo no no i think a minimum is twen 20 milliseconds is i yeah yeah it is right 20 milliseconds 125 milliseconds is kind of sift this is maybe because of f f t u or these things from 0 yeah 20 millis minimum is no no de because of it is no not as small as as it should have been but mayb maybe because of other processing after i do not know i just no you can use mol yeah no mainly because of m f c c computation because yeah it becomes really noisy like if you s yeah no yeah well noisy yes 10 milliseconds means you have only 80 samples for and then you have refused 24 filters the filters will not get any samples so right sure yeah okay but it is not an intrinsic limitation it is just because of ri yeah yeah because of computation so even i i think even saint he can find less than 20 millisecond windows also especially for plosives okay and the shifting is still the same or not shifting yeah he is using 125 millise yeah shifting is almost same overlap you mean 0 yeah yeah overlap so but the problem like w how do you mean to say like no how many frames per per 2nd you do have is it again like 100 frames or it is less or even if you keep variable length of the frames right he yeah you can still keep the same frame rate yeah he is keeping same number of frames he is using 125 yeah 125 millisecond because i understand this would have sense for i do not know speech coding where you want to preserve or you want to encode it into less frames right but why do why to do it for speech recognition yeah it means that number of frames yeah no wha why to keep variabl variability in the length of the frames w you want to wh why not to keep the same length what is the actually the problem is again you see the shift is the nyquist frequency if you compared modulation spectrum yeah so again if you change this shift the mod the nyquist the modulation spectrum nyquist frequency changes for each window right so if you want to do again another high level feature extraction again it will be problem so so but he is like he is he discuss i mean describing this because even this itself is a problem keeping the f fix to frame size because you are analyzing your this segment many times right right right so this shift will suppose if you find one segment of 60 milliseconds and then you are doing this 10 millisecond so every almost i think yeah yeah 5 frames y you are analyzing the previ this already segment so right right right ri right so again this may blur some frequency transitions or yeah but the problem is again this bottleneck is here like you can not change your nyquist frequency modulation and right yes i have 2 questions 1st is essentially how he is doing in that no well once he gets the frame like from here to here then where he advances to to start with c computation of the next frame yeah yeah yeah so it is so so the main idea is like 1st to take the some some window some sam some samples then he assume somewhere some variable and point there is a change yeah okay so it is not n it is not symmetric necessarily then then it is not symmetric i you can cho y it is basically too like again do for all the samples in that gyro kay so then what he does is like he proposed like some like ratio test based on maximum likelihood okay so th tha that is really simple like so what he does is like so he he computes the residual he 1st he does the l p analysis and then he computes the residual okay and he takes the residual energy of f full signal like gy gy gyro samples fu ok okay so do what do you mean full the full signal so maybe i can some some sub window or the the whole 10 seconds of speech or what the wh the whole window no that is that like he assumes yeah 60 milliseconds or something he start with so so then he can yeah yeah it has to be also some somehow okay something longer window than the one we are speaking about okay yeah then he can split that window at point n say point n so then you will have gyro 2 no you can segment it maybe w okay so you have a window and then you split it to s 2 parts and you shift the the point where you split it or okay yeah so yes he is supposed this is your p window so then you can move your like this you can move your point so that then this will be like one and this will be 2nd okay splitting point okay okay but again he assumes some initial sizes for these windows okay so basically we have 2 fixed points yeah he do not start with the yeah he do not s yeah start with gyro sample or something he is starting with so the l left window is starts at 20 milliseconds right yeah yeah okay so so you already hear like s suppose this is full signal you are already here so right window is should end at 122 so you are to search basically in this range okay so okay so once you have say one hour of speech you start at the beginning and 1st you try with 20 milliseconds yeah yeah then you shift to 25 and so forth until you yeah and then if you the maximum is 60 millisecond frame for him okay and and you find the point where where what so if you do not find anything where where do you stop actually 60 milliseconds if you d yeah i know but you start at 20 and go to 60 but where which point do you choose no the point is depending on the likelihood ratio so he got so he supposed this so this is suppose se segment one and this is segment 2 okay but which which likelihood ratio so he compute this error here okay so and then he p gives some kind of in terms of this residual letter so this residual letter is for full gyro samples then there would be suppose this point is say n so gyro to n and then like so this the b basically the main he questions or here so he is maximizing that or yeah he is for finding the likelihood for full frame and then it is basically likelihood ratio test so he is comparing the likelihood of the full segment divided by the likelihood of the the sub segments yeah but likelihood that is something and something w what is likelihood likelihood of the like this likelihood is this error estimate of the residual okay so residual power he can compare t so he after do after doing l p he can get a l residual and then he can compare the power of the residual yeah but it has to yeah so that he is proving that power is again maximum likelihood estimate of your l p parameters okay so it is basic so that which interesting it is you do not really need to do a lot of computation yeah you just need to take the error of n residual of this full window and then residual of energy of the sub windows and then you can just divide them and then the only thing is like again he has to use some threshold to decide so right yeah yeah but still that that is only the p problem if he got he is founding something more around 3 or 35 e s t optimum threshold but the and another advantage is like it is not really changing speech recognition whatever so not really changing because of the threshold so okay you do not really need to f like fiddle with threshold a lot so yeah but why do the main value which are comparing to the the the overall value or overall likelihood or whatever you call it why he is using the whole signal for this yeah yeah yeah because so if the whole signal was steady somehow and it it would be able to be modeled by l p model yeah well then the residue will be very low so he is supposing that the whole signal will not be able to to be captured by l p model and that the residue would be high enough or ho wh why he is comparing the 0 to to the overall yeah yeah this is yeah i understand this is this is the actually basically some theoretical proof like maybe if you want threshold you can see and he is coding from leven like you know i i do not see the point of dividing over the the whole stuff no this is from again yeah statistical signal processing what they say here is like suppose if you analyze 2 distinct or this l p analysis in the same stationary analysis window the coding will always be greater than the ones resulting from this analysis in 2 windows 2 stationary windows y well sounds sounds reasonable so the he is basically based on this theorem so so what he is saying is like if you do this error i see i see i see well it will be always greater than the th these errors of 2 stationary windows okay that is reasonable because actually if it if if the whole signal would be able to be modeled by l by l p c then anyway he has to design some some framing yeah yeah so even if it is very stationary by d by dividing over this whole stuff he is able to find some reasonable boundaries yeah yeah probably m ma makes sense yeah yeah yeah yeah yeah this maybe i think yeah this he is getting some improvement but okay yeah ho yeah and how did you cope with what you mentioned some time before that if it is a very long session for each 125 frame you will actually get the same segment now yeah yeah yeah yeah so how d so he is just he is normalizing the energy coefficient because energy is really affected a lot ah okay so he is li that has to be power not energy definitely no energy like because c gyro component like so he is using m f c c so he is normalizing the c gyro component for this t yeah yeah but it has to be normalized by the length then power instead of energy power is again like yeah yeah yeah yeah it is time like yeah energy over length is power it is okay like samples or you can see root mean square energy or something yeah yeah duh duh duh duh duh okay so but this so 2 things i still do not understand thi this is supposed to be only for m f c c s and these easy features ah one question i i wanted to ask before yeah yeah does does he preserve within the feature vector the framing he is using finally because does he somehow put the information about was the size about the frame to the feature vector itself or he is throwing away this framing no i do not think so no no n no you vo what you mean like the window lent there is no information something like this because the recogniser could be good for the recogniser to know what was the chosen framing by this extraction the 60 mil no it i it does not really but i think it is he he does not use i guess because then like you just you take these features and you train model so it really matters wh what frame shift you are using for models like because how many he needs to use same freq okay so so it n it might be bad for then for the back end it might screw up the the t speech rate or normaliser no no wait no he will get like every 10 milliseconds he get one fra one features like he it does not really depend because suppose if you take your t case like you your window i maybe longer so does not really matter like how what size window you use or not but i tell at every 10 milliseconds whether you give some features or not really matters no like so so it doe you can use 30 milliseconds or 50 millisecond window huh 0 probably i did not get it too so the framing is actually equi distant same yeah yeah yeah just the windows are wider 0 ah i see yeah yeah yeah yeah yeah yeah yeah yeah right right right yeah yeah framing it is like it is really yeah because it would be crazy yeah a little bit yeah if you have if you are change framing it is really i think even it is really problem no yeah yeah yeah it might be difficult 0 i see so so it is it is like this like if you try even models also i guess if you change it is not only with this model it is in spectra and shifts in deltas because probably who knows maybe it is possible to use it somehow but then there is information about temporal information is somehow included in such yeah yeah but it is really complicated i think if you use yeah that becomes really yeah i cannot imagine the transformation to get the modulation spectrum out of such a stuff yeah yeah but this is kind of interesting there is also some like not paper but i saw the algorithm i have been using even that called temporal decomposition i do not know if you kn you know that like yeah yeah yeah the temporal dec i do not know who propose it i just had it from from beambot for like a beambot do you know the guy he is french somewhere now in teleconference like i do not know where he is working but th we were using it for speech coding yeah yeah so we just had a speech and you had decompose a speech into such segments temporal segments which were stationary inside yeah and then you can more or less quantise those segments somehow and use it for encoding let us say or and it worked prett yeah yeah yeah yeah yeah 1st this is proposed by like even we read one paper in our reading group you remember that i have implemented it is quite working well like yeah they were using like s v d stuff singular value decomposition for that and it worked pretty well like i was surprised like yeah yeah honza is also worked for his be yeah yeah yeah exactly he was using that i yeah yeah so why do not you still use it ne he was re he was quoting 0 well it is it has been used for speech coding if it works so well nob nobody use it for recognition stuff i think i never heard that yeah but you do the speech coding so do you still use it but that no i do not of course why yeah that is good question maybe yeah he was quoting that paper also like v he was telling this this is kind of optimisation criteria what this temporal decomposition is doing like you are trying to segment your s signal into like discreet windows and then so but he was telling like this relationship between optimation and and then cautious stationality is not really obvious right right so stationality is again different no stationality is this is optimation we want to like see some signal yeah that is true few segments which can really represent whole so maybe that is why like this may not be really that is true it is interesting this yeah yeah this one yeah he is getting some improvement n this is a yeah that is important well there should be after each conference there should be something like 10 people should sit down read the papers and then rank them all the papers you know a all the all the all the similar papers within one session say sp focus to this topic okay and say this is the best this works slightly worse than this one and throw away what is not that good and and th the 1st 33 papers which are the best shou should be implemented and used from from that time onwards i think people would not like it many people would not like it no no no they again like forget the all the m f c c s because it is too old and then start building the story on the new stuff and not cause it is right but again getting that 10 papers is really difficult task it takes maybe years and again it definitely like who will who will choose the 10 papers like and if we asked yeah yeah it is but i know i know th this is just a theory yeah yeah yeah sure exactly but yeah those things are practically kind of really but i think this kind of things what you showed i mean what you said is really good because like if people start implementing like some people propose something in feature level some propose in something in model level but these 2 are really independent no yeah yes and i want to yeah when really work combine these 2 or right and everybody is like using different training and testing data and then you do not know if like yeah especially features like suppose if somebody come with different good features again people saint again use m f c c s yeah they show it works for timit but then you try to use some different databases you see it does not work it looks like no no it it is not really sure also make suppose people you can not really force people to use same features yeah sure so so they wo they will be happy with their own features and their own scripts so their bit rate like tend to change features every time and it is yeah yeah you can sorry what it is a little bit what nist is trying to do no yeah exactly yeah yeah yeah yeah so nist evaluations yeah yeah bu because to me it seems that everyone is still comparing to p l p s or m f c c s but even nist you can and there are so many new systems and everything is new but it baseline is still t 20 years old and yeah but nobody you know it is those new systems are still not general like you know yeah like those p l p and m f c c s because everybody knows that di it worked somehow right for any kind of data yeah yeah yeah that is why they are comparing to to that so once somebody will come with something new okay he he is showing it works for some datas but still yeah yeah yeah then so at lea especially l v c s r in really big systems then people no okay so 1st it is difficult to to show that it works for all all the datas right because it is really and yeah yeah because yeah it is it is again you do not know so it should be every papers then should contain all the results from nist evaluation or some standard task then 0 no then there will be only one conference in 3 years or something for speech because otherwise yeah but then we all optimize on the same task that is the other yeah yeah so you cannot get anything new out of that after some time but again like okay so it could be you you would have to publish the results on this standard task and then you could say well i also tried on this and this yeah but then you run out of time that is so you can publish only once in 3 years or no i ideally you are correct but yeah you know but then then only it makes some progress this yeah but even people are doing suppose in interspeech there was some challenge for speech synthesis so so what is is like it challenge was really good like sup they give the database so you have to work on that database what technique t use is it is your choice like but that database and then the results analysis is they decide so yeah that is your choice even speech recognition also some tests are coming like for phone segmentation or something people give some database and then you have to dis you can use whatever you want and you have to produce even for features also like features also i think yeah but can for for example can you use from some data from from the l t world yeah yeah the they they designed the data such a way that it is it is really like really real data for training say yeah but they give you also training data or only the testing data no no i just for no no for s nay speech synthesis it is like they give you some data 0 i since this is only okay so whether like you use for training or l you can you can do like data driven or like model based approach or whatever like at the end then they will ask you to synthesize some sentence and you have to synthesize them and then you have to send them okay so so they give you only the training or development data only so yeah your training data onl no development training no yeah training yeah ho how how do they how do they evaluate yeah evaluate it is again subject to s because for speech synthes yeah i just do not know so they ask peop they ask people to sit and listen basically yeah listen and yeah d yeah even they it is mostly they use native speakers only because they can really judge well so i think so yeah probably yeah okay but they d they do not have any measure like you have an original speech you transcribe it and yeah yeah yeah yeah yeah yeah yeah so even they propose some kind of word error rate so you have the original speech so i the synthesized speech is there any words which are not matching so even speech synthesis they are also introduced this w e r term so okay but this was really quite successful like in this conference interspeech so a lot of people participated in i think even they are continuing this for next year and ri i it is just t t s right text to speech text to speech right but i heard like even for i cassp next year there is some com like computation by martin krug sheffield and on this feature extraction stuff and so at least if you make task simple focus then it may be good to compare these features and but if you s use some large vocabulary system it takes t 6 months to build and at the end you do not know whether your features are really so right right so maybe that is for like people are always using p l p s or m f c c s it is it is such a b like lot of time involved so you can not really check many thi so maybe well but then well no if somebody like nist will have one recogniser and you just plug a different feature but it is dangerous yeah yeah yeah yeah but at least in idiap we have this numbers recogniser is almost kind of free so everybody is using so that is what we are doing no right almost almost like we are putting different features and i think yeah yeah numbers recogniser is not really different from like a f at least in idiap we have almost same yeah yeah al almost but maybe you are using 29 that is for you it is different but one more yeah we are a couple of people at idiap and there are already 2 s 2 different setups but at least i 27 hem hemant is using difference are they for 0 g i s hemant and we are all using 27 so for 0 g i numbers yeah stories and numbers or different setups like some people use this setup and some people that yeah seems yeah i think we use di distinct train and test set even se the different sets of phonemes no but the problem is you are using only digits so maybe that is how you different well i do not use only digits my m l p s are trained on everything and test set is digits yeah no but test set is digits your m main task is digits k yeah huh that is like but 0 g i numbers are there like now we are little bit convulsed like we are using 27 phones and before it was like 2425 but it is better like once you have like whatever the back end so you just then you can put features like whether they are gammas or like spectral entropies or m f c c s or whatever yeah would be would be nice for aurora maybe to have that then at least you can see yeah yeah yeah for for what but aurora is aur aurora aurora task ah yeah 0 yeah so but aurora is it really big database or i mean how much time it takes to set up system and then no no but yeah yeah no it is it is fast no i dunno i i have been working with that and but the problem is aurora again these models are word based models no yeah right yeah yeah yeah so again here we use triphones and so but definitely i d if we want to show noise setting it is you have to show on aurora also like aurora is real time yeah for noise conditions yeah it is pretty good well th those f recognisers are are made there or just you just use them that is it yeah you do not have to play with it she even is f it was forbidden in the time right so you have recogniser just use it yeah yeah you cannot play with that do not play with it you just play with the features yeah yeah yeah because of the voices would not be comparable then but yeah yeah right exactly yeah at least i mean wha those studies i think people already did for even in i c i c s l p 2002 there is special session on features for aurora so yeah yeah ri right so yeah yeah i yeah yeah yeah yeah i remember with david pearce he was this icsi features and this s s 0 g i features and all this yeah yeah exactly so there are already people but again like at the end like for l v c s r people are using p l p s or m f c c s or nothing of these fancy feature because who is the one who makes who actually uses what we d try to do here these are companies or because it is not scientists scientists just start from p l p and then design the new features new back end or new anything but they start from the old stuff but who is the one who is using these results which we publish and try to make yeah new but maybe my have a f for publishing another paper or something we are using those results more or less yeah but once you once you finish your p h d you go and it is over but not everybody go yeah not everybody many people stay so and those who do not they stay and publish another papers and they but they they do not turn into a life no but maybe guillaume you can tell no into life like you worked i with these siemens people or no siemens or these daimler chrystler yeah daimler yeah what kind of features do you use like do you use any you do not yeah you do not i do not know you know they are extremely secretive ah ok okay huh i had a hard time just to get the signals okay so i asked other questions but it is quite tough yeah ah ok yeah even s yeah yeah i did not discuss anything with sunil also like i dunno technical discussions anything yeah because these kind of things are a bit yeah yeah they have to yeah yeah yeah i i won but do you have any like feeling that whether they go for all these new features or they usually use only yes i wonder which level they are using out of what is there they have yeah they have to do some test in their mercedes some speech recognition test but it seems now they at least daimler maybe yeah they mostly work on dialog management but not much on features and recognition but even they do not believe like with one paper or 2 i guess because at least for them no p l p s or m f c c s no yeah sure they know that okay these things work on every task so okay we can pu put hands on these things yeah but exactly yeah yeah i d i they grab i doubt they are trying right now huh i doubt that at daimler they are trying right now but maybe other companies yeah yeah yeah yeah maybe like yeah f because sunil is working in nokia he is a expert in kind of features or yeah it l i think okay so we can hope that nokia will be using tandem tandem p l p s but yeah we are not sure even sunil will not tell us no yeah i cannot well many people place with tandem right like m i t or who is using that some not m i t but somebody no no n no this cohen you mean like i dunno hynek told told us that somebody is using that very actively like okay it it works for them and and they are close to like industry like you know there is it is a research group of i know i do not think it is icsi 0 ok huh so it is not related to icsi huh somebody else or i do not know somebody new s but i think some t somebody like a t and a t and t or what but this was signal company no like this voice signal company hy hynek always mentions cohen is company with what they are using you n yeah cohen 0 qu yeah jorda jordan cohen you me i do not know he is even if qualcomm voice no no voice signal no no no they make recognition for these samsung phones and yeah now y 0 yeah yeah for right right no i do not know if it is yeah but it was they have like really s many recognisers i guess yeah i think so maybe definitely they may be using p l p s or you know yeah definitely maybe they are using p l p s and but i th i believe that there is still some stuff up to yeah yeah rasta or rasta or something you mean for rasta or whatever else like all those decorrelations and transformations yeah because i think it is there but more or less it is again based on m f c c s or something like that yeah but again because they grab whatever is certain to work and they they use it or maybe traps they do not use anything which is to too new which is not that safe yeah too new is really definitely because like want to at least see i at least 4 no more or less i think yeah 5 years of results of some new features and some consistent results or it is like a it is like a nasa you know they use 48486 machines to to to to put 2 n space ships because it is it is known to work perfectly well or pentium ah space or ah okay right not any p 4 or whatever because it is safe it has been working for 20 years and then only it safe to put well not 20 but it is always yeah yeah of course yeah yeah yeah that is true it is true but that is different situation because right yeah they need that it does not create that much yeah of course if something happened with the machine and i do not kn the alloc system it does not matter right but yeah d yeah another issue is like again the computational issues no somebody will fix it and change it but yeah if they want to make on mobile they do not want really use some fancy really expensive computationally expensive features or all these sure hynek also told about this cohen ce cell phone which he was showing to to work l with the l v c s r yeah yeah it yeah he he was saying that there is something really really simple the decoder i mean not the features but he was mentioning something to the decoder this is just there is almo it is just phone recognition or something like even even not viterbi just even something simpler yeah something like no d t w or something i do not know i am not really sure but he mention that yeah yeah but most of the time mobiles they use d d t w kind of thing no like all these so voice calling yeah voice calling and these thing because that is easy like i do not know do you think so yeah 0 yeah yeah yeah yeah that is true because they do not really need to put lot of memory and these things otherwise if they want to build a real yeah sure yeah but there you could send an s m s i think or something like that you could dictate text so yeah yeah yeah where where that was impressive even on the phone on a phone yeah yeah yeah n some new one or the phone that cohen yeah voice signals from that c cohen is yeah cohen yeah yeah yeah but it is that is a similar one to what herve has for example some he was showing it live on m l m i c last m l m i conference 0 huh yeah and but which phone it is you can look at the video and play it it is samsung some some new really new phone like f samsung something it i am not sure where some but some which you can buy right yes commercial phone it is yeah but the software is always available or like it and you can train it like only your speech or it is general it is quite interesting yeah what do you mean always no the software is again on top of mobile or like it comes with your mobile i do not know that is well they they just bought a mobile and based on the 0 s it is using they they they developed the software no the thing is usually these kind of these kind of facilities are like no not normal so so they may charge more or like yeah yeah no this is the development phone this is not the commercial stuff with the software yeah ah ok yeah definitely they may charge for more money f again some extra bill for this using this recognition engine or something okay huh they just use the device or you you can just buy that application right yeah yeah i do not know like it is like you you have got some mobile phone by by what and then you can buy from another company some such application wh you can dictate your s m s or whatever yeah i do not know if it is already the case right now s i do not know if it is done now but like yeah but maybe like if it can not really recognize you it will be really annoying it is better but have you ever used like dictation system for windows those there were many of them yeah i have used once like this dragon that was kind of good like yeah yeah drag the mouse there and i heard that it worked very very well because even in when i was working in like in edinburgh like one guy he use i he got some problem with hands and then he was always using this n dictation even he used to write lot of c +plus code with the dictation like alright it is really good like so but you ought to really train and then you are to kind of yeah right newer speech okay you had to get used to the system not the system to you i get used to the system yeah yeah it is really like you ought to get used to the system okay okay like you how to say all the callings and this thing system how t but it is really funny like how to say okay he is able to use it for like like years i mean he used to write a lot of code and you know it is really great it is nice by the way you should be the one to ask is there anything if i am just a p private person and i want for free any software which is able to read me a book in english for example that festival am i able to put the text to the system and to in real time to listen to the output yeah yeah you can listen festival festival is able to to to read just a normal plain text in reasonable comprehensible english or so that i can understand it read the like yeah i think yeah yeah definitely yeah yeah fes yeah i think i think you can do the festival you can just call like this there are different kind of again non native speaker how you call the festival so you can just give the full there is an too okay so then you will get the all the caller and other things if if you want to like read full text then you can say like the full text yeah like those syntax and then it will speak till it finishes the text and it is in real time on a reasonable machine yeah it is real it is yeah it is based on the diphones diphones right diphone yeah yeah that is why it is real time like otherwise it is bit difficult and it is okay you can easily understand so it is intelligible so yeah yeah okay yeah i think it is very good yeah okay festival is good like now it comes at all the linux boxes also so in so already in this cause the the diphone that i was tried to install it and there there are the voices like cat voice and these voices this is not the one no the there are so many voices like again because i do not know which which what i what is running where if it is diphone or which approach this is which voice you want to use no most of the the festival what you get on linux machines is diphone based only yes so it is just wha samples of the diphones and it choo chooses so they supply few voices yeah yeah yeah just concatenation of the no it is already there if you just put li festival so you have got it installed in your machine here or yeah on my on my laptop it comes wi because it is comes with direct linux software and you know it is open source or here festival right okay festival is open 0 yeah you can you can easily yes i d i have tried a and have you have you tried it it is and but there are some built in like easy voices which you can y there is a default one completely synthetic so you can which you cannot understand at all and th then there is a diphone synthesis i think it is the diphone synthesis from the some like something like cat w voice and somethi this is so so yeah cat voice like there are like they they have few but it is this does not sound very nice and we were playing at the summer school with them actually with the authors huh and they were playing us some other other approaches and it it sounded like a human it was great but yeah new voices like yeah yeah that is the difference that tha in views this unit selection it is not diphones you have really large inventory of the speech yeah so this is not this is not for free or something those voices they are not yet released yeah no no it is free they will be releasing soon i guess but it it is not here so far so maybe you can just check no i it is it is here on the web then it will not come with your linux software so you have to again download from their web page okay i am i am fine with this but i can make it run on my machine yeah you can download now yeah yeah yeah it the voices are called multisyn multisyn sl that is some voice okay okay so you can download from the voice from the web festival and and the engine is is in the festival itself yeah yeah yeah yeah so it is just a voice to to okay yeah yeah yeah so voice is like the database of the parallel speaker who speaks that this text so okay it was really large database so it the voi the quality will be better because you can find similar yeah so it is still still real time it is more or less but not exactly like it is 2 times or one5 times or something ah okay but but not much yeah but you can prune like how much you want because yeah yeah okay so this is again like that is kind of my p h d work how in this system like how you choose the units and how you concatenate so there are again cost functions and viterbi yeah it is very difficult and so you can you can always optimize you can put some thresholds and pruning and then you can make it real time yeah yeah so then again compromise between the quality and yeah alright yeah yeah but still it is good like now th you can just check multisyn and then voices okay it is right i mean yeah yeah it is really good and you just have chosen some text any text you just put in and yeah i just s use the douglas adams that that book you know the how it is called yeah yeah yeah yeah but only sometimes like it can not really call this yeah okay just general english textbook i i cho i tried and it can read how does it read from which from which how to state it is it possible to read p d f s or just standard text no the t t x t standard text whatever t x t right files yeah yeah ascii file is yeah ascii file basically huh you have to but you can take a p d f and convert p d f to ascii ascii and but only the problem is again acronyms and sometimes it expands sometimes it may not expand because it is not in their dictionary so yeah yeah well let us yeah it was a line with hashes and it was hash hash hash hash hash hash hash hash hash hash 0 really it is not in the dictionary like if we again it is problem like so but those things you can add like if you are really familiar with festival so you can add always these i am not then i do not want to go too much into details just a little bit yeah but at least these things okay like it will not really make mistakes so often like sometimes well i can prepare s sorry anyway who is the author of festival alan black and paul taylor they started r festival in 96 or something these are the edinburgh people no edinburgh c s t r yeah edinburgh and i thought it that is the guy from c s l u from portland who was there during f f 0 this ma this johann waters or like no they d what they did portland 0 g i they did l p c based synthesis yeah but there was the festival is alrea already like it is 11st you start with th no no but that that was the guy who is on a wheelchair now he cannot m move even he is very in like indic how to say that like he cannot have he just can speak only through some festival system or something like that ah okay he was the auth no yeah h he was working i think in w 0 g i even from he is very famous but i thought that he was the author no i do not know no no actually the b it was developed for him like he was just 1st tester i think of the system ah okay okay something like that yeah maybe because like it is not t text to speech it is just the synthesis which means which is more or less the same but i dunno ah ok yeah 1st they started in c s t r with alan black and paul taylor then then it expand to many places yeah so it is somebody and simon king is playing with that right the t guy because if somebody is fil no simon king is he is one of the others but rob clark is the man that you met yeah voice already okay okay that i know yeah it is i think that is enough yeah n so anything more about lisbon or interspeech no other papers it is just one paper and and festival i it is enough yeah from linux it is very im interesting okay okay n yeah
from now on you will be quiet i think jitanda is going to talk about it no i am here to take care that you are speaking i never know how to do this it is always the other way around that how you put it on yeah okay i think the most of the bad words are at the beginning at this precise time yeah so you start i think right so we all attended interspeech no yeah so we are going to talk about the papers from interspeech so how many sessions you attended you attended that is not a good question i think all of them of course all of them of course 0 you can not attend multiple sessions at the same time 0 yeah that is true that is why you are okay bad answer right so any general impression or feedback so for any of you it is a 1st conference or or maybe for most at all i mean in the sense that you say interspeech is really big conference for speech so did it even before well january that is why i am here at the other because i did not know yeah so then you i think you have attended before yeah we met in also in icslp right the 1st time so yeah so how did you like the conference anyway when you compare to to the others the preview one yeah i think it is quite interesting but only annoying thing is these multiple sessions and sometimes you can not able to go to oral talk this warral presentations most of the time why why you can not because like posters is you can spend a lot of time posters looking at many posters than sitting 20 minutes for one oral presentation so in 20 minutes you can see at least 23 posters and you can directly talk 2 people even i found like very few people in some world presentations i think most of the people are like on posters okay so you attended mostly posters i guess but how many presentations are hardly handful of like 5 or i like the invited speaker about the implant in the year 0 yeah it is quite good he was from australia right the guy i think so yeah yeah yeah he is very famous i think for that right he is the inventor of that implantant for the year i think even some panel discussions on that human speech recognition reducing gap between esr and h s r it was a bit interesting a lot of arguments definitely it is mainly the differences different approaches of engineers versus language versus linguists or phonetians and yeah i realized that there were panel discussions only at the place where i could see you know there were some boards saying that there is some panel discussion i could not get no one knew where is it what it is when it happens and why and so forth yeah even said the panel discussions i think one is really held in small rooms so people were really growled there wow this is not this speech is not restricted on the 2 papers and these things you know so how did you like this upon that not quite good there it is a bit hot still yeah the beach nearby is quite nice yeah if you try it i do not know did you go around before or after yeah we went to do custac aparica there okay by the past we were supposed to take the direct one but we did not so you went to a beach yeah like atlantic sea coast yeah i stayed 2 days before so i went you just take the train maybe that is the same i am not sure the train north i see so far from a 3 from a 3 north yeah west or north i do not know okay at the same coast not you did not go to the island cross the river no no no no okay so it is different we went to like far south to lagos yeah and yeah with hamat 0 him mountain that was really good those beaches are really good yeah more south to yeah even though the water but weather was really hot the south it is more than 35 yeah but i think lesbun those days were pretty good lichmann was good seemed to me quite not cold but still okay like reasonable yeah even local transport 0 yeah it is fun like but yeah most of the time the buses are really crowded yeah i was happy to be back in swiss after a few few days it is a better to be here i think yeah because it is really big city yeah it is big city many people i was very surprised that even the trams did have ac there yeah yeah i could not see it those new only right yeah but right but can you imagine something like in czech ki yeah it is not necessary to have it maybe well sometimes yes but not so many days if you would have to spend an hour and a half that the hemant and the others spent while going with a car with a bar to the conference conference center from from the hotel that we were there was a direct bus and it took them no but that is that is not good that was not good like so you can go to the that is a central place and then we go to take the tram and a train okay yeah yeah it is all the right choice to take something which which was direct yeah the easiest way not definitely the direct as anything else what is that place caisho cash cache the central i mean where we changed the bus i do not remember the name is he there no it is there caesot yeah cajos center i am not sure yeah maybe you can check the book it is there i used to find bus number from vokola originally i brought it because i marked some papers but go on yeah only one which surprised me a bit it was a speaker recognition i think but it does not really matter they were trying to model covariance of different components of a gmm instead of using a diagonal but then if you use a full it is too much so they use some approach where they tie them automatically and after that pruning of covariance much well it is like tying it is still full but different components are tied different components of the matrix are tied what does it mean tied then equal like yeah but then you have to store the whole matrix and plus some extra information what is tied to what no no you have to store a minimum number a reduced number of parameters and they are tied in a linear way actually sorry not equal but it is like all the semi tight covariance matches yeah exactly so they reduce it is in between the direct covariance versus full covariance so you try to reduce some parameters by tying the interesting thing is that they applied it on speaker recognition and on the features before they had 3 different way to decorrelate the features and they showed it without this with diagonal with diagonal gmm or with this and with this with semi tide covariance matrices it was a less much less sensitive to which decorrelation procedure you choose so so they obtain better results with that finally also better but of course it depends on the number of parameters you put right in your day but if you compare no if you compare it with some baseline let us say you use just gmm with that i think they got good results in just using diagonals or differently i think it was even slightly better yeah and no decoration for like maybe decolation again they do dct or klt or they do that well lda is the domain point is to make it faster the decoding or what make it possible to use full covariance matrix yeah but there must be the sense right because once you decorrelated the data then you do not need to have no but still there is still even that is where people will come so that means that the decoration is not optimal right yeah definitely it is not optimal so it is also like doing along the diagonal so again so it is completely impossible to do it with full covariance matrix why it is really competitionally really yeah yeah competition so see suppose if 39 to 39 for every of course yeah so from the point of view of competition but otherwise it is otherwise i think there is no problem no there is not no problem but if you see so many models and so many mixers so here you have to choose either many things to store and huge computation you can see you can not really like even you can not the models itself is like 38 times more than than that so if you have like 5 megabytes or 10 megabets of more depends but even i think it is really like impossible for the really big systems yeah definitely improved coveyons model almost impossible for like lvcsr it is impossible to use it right you have got dimension 39 times 39 yeah for every year so you have to store all this so there are no it is impossible to use it and you cannot even train it no but in the proceedings you can find them they are better they tried pc ldm and llt 0 it is not really anything new but they just applied that to speaker id yeah different tasks yeah so i found one paper interesting paper interesting i think you know vivek most of you this is vivek is paper on variable scale feature extraction normally we used to fix it window for feature extraction like 25 milliseconds or 30 seconds here he is proposing variable scale the fixer scale is non optimal for it is not optimal because like for vowels you can have much longer much longer and for plosives and things you really shorter like even around less than 20 milliseconds also okay so he is proposing like this variable scale window kind of online for each segment why it used to be online yeah you can do like you can measure and you can do but he is basically doing some likelihood ratio testing so if so the main idea is like suppose you have a one segment so he is trying to find the stationality cost of the stationality of that segment right like extend it as more as much as possible as much as possible like okay but yeah definitely you have to assume some the minimum and maximum sizes of your own so he is using what is the name minimum is i think to around 125 milliseconds i think maximum is 60 milliseconds so is not it smaller the minimum no so the minimum 25 milliseconds i think minimum is 12 20 milliseconds 20 milliseconds 125 milliseconds is kind of shift 20 mil is minimum is i do not know because of not as small as it should have been but no you can use small other processing after no mainly because of mfcc computation because yeah it becomes really noisy like if you is no easy yes yeah 10 milliseconds means you have 80 samples for and then right you have if you use 24 filters the filters will not get any samples but it is not an intrinsic limitation it is just because of yeah because of competition so even i think even he can find less than 20 milliseconds windows also especially for and the shifting is still the same or not shifting yeah he is using yeah 125 milisier shifting is almost same so but the problem like how do you mean like no how many frames per per 2nd you do you have is it again like 100 frames or it is less or even if you keep variable length of the frames right you can still keep the same frame rate yeah he is keeping the same number of frames he is using 12500000 i understand this would have sense for i do not know speech coding where you want to preserve or you want to incobey into less frames right yeah why to do it for speech recognition it means that number of frames yeah why to keep a variable variability in the length of the frames why not to keep the same length actually the problem is again you see the sifties the nyquist frequency if you compute modulation spectrum yeah so again if you change this shift the mod the modulation spectrum nyquist frequency changes for each window so if you want to do again another high level feature extraction again it will be a problem so but he is like he is like he is he discussed i mean describing this because even this itself is a problem keeping the fixed frame size because you are analyzing your this segment many times right so this shift will suppose if you find one segment of 60 milliseconds and then you are doing this 10 milliseconds so every almost i think you have 5 frames you are analyzing the previous this already segments so again this may blur some frequency transitions or yeah but the problem is again this border link is here like you can not change your nykris frequency modulation and yes i have 2 questions 1st is essentially how he is doing it that yeah well once he gets the frame like from here to here then where he advances to to start with computation over the next frame so so the main idea is like 1st to take the some some window some samples then he assumed somewhere some variable end point there is a change okay so then it is not symmetric this is not symmetry you can see i it is basically how to like again do for all the samples in that 0 to young so then what he does is like he proposed like some likelihood ratio test based on maximum likelihood so that is really simple like so what does is like so he he he compute the residual he 1st he does the lp analysis and then he compares the residual and he takes the residual energy of full signal like these 0 to end samples okay so the full so maybe some some sub window or the whole the whole 10 seconds of speech or what no that is like he assumes yeah 60 milliseconds or something he started with so okay so then he can longer window that yeah yeah yeah then he can split that window at and say and so then you will have 0 to no you can segment okay so you have a window and then you split it to 2 parts and you shift the to where you split it or yes see so this is your window so then you can move your like this you can move your point okay so that then then this will be like one and this will be 2nd but again he assumes some initial sizes for these windows okay so basically we have 2 he do not start with yeah he do not start with 0 sample or something he is starting with so the left window starts at 20 milliseconds so you already hear like suppose this is full signal you are already here so right window is should end at 12 at 12 point so you have to search basically in this ring so once you have say one hour of speech you start at the beginning yeah and 1st you try with 20 milliseconds yeah then you shift to 25 and so forth yeah and then if you the maximum is 60 millisecond frame for him okay so if you do not find anything where where what where do you stop actually 60 milliseconds yeah i know but you started 20 and go to 60 but where which point do you choose no the point is depending on the likelihood ratio so he got so he suppose this so this is supposed segment one and this is segment 2 so he compute this error here so and then he gives some kind of in terms of this residual error so this ratio error is for full 0 to n samples then devoid by suppose this point is say n so 0 to n and then like that so this is basically the main equations over here so he is maximizing that yeah he is finding the likelihood for full frame and then it is basically likelihood ratio test so he is comparing the likelihood of the full segment divided by the likelihood of the sub segment likelihood that something and something what is the likelihood like the likelihood of the like this likelihood is this error estimator error estimate of the residual okay so residual power he can compute so after doing lp he can get a residual and then he can compute the power of the residual so that he is proving that power is again maximum like load estimate of your lp parameters so that way it is interesting it is you do not really need to do a lot of computation you just need to take the error of the residual of this full window and then schedule of energy of these sub windows and then you can just divide them and then the only thing is like again he has to use some threshold to decide that is only the if he got he is finding something more on 3 or 35 is the optimum threshold but the another advantage is like it is not really changing speech recognition order so not really changing because of the thresholds you do not really need to like fiddle with threshold a lot soon yeah but why the the main value which you are comparing to the overall value or overall likelihood or whatever you call it why he is using the whole signal for this because so if the whole signal was steady somehow and it it would be able to be modeled by lp model well then the residue will be very low so he is supposing that the whole signal will not be able to be captured by lp model and that the residue would be high enough or how yeah this is the actually basically some theoretical proof like maybe if you are interested you can see i think he is quoting from 11 i did not see the point of dividing over the whole stuff yeah statistical signal processing what they say here is like suppose if you analyze 2 distinct pith order this lp analysis in the same stationary analysis window the coding error will always be greater than the ones resulting from this analysis in 2 windows 2 stationary windows so he is basically based on this theorem so what he is saying is like if you are saying if you are saying if you do this error it will be always greater than the errors of 2 stationary windows okay that is reasonable because actually if the whole signal would be able to be modeled by lpc then anyway he has to design some framing so even if it is very stationary by dividing over this whole stuff he is able to find some reasonable boundary boundaries yeah probably makes sense some yeah yeah yeah yeah this may be i think yeah he is getting some improvement but how did he cope with what you mentioned some time before that if it is a very long stationary period for each 125 frame you will actually get the same segment now yeah yeah yeah so how so he is just he is normalizing the energy coefficient because energy is really affected a lot affected a lot okay so he is what is to be power not energy definitely you know energy like because he is c 0 component like he is using mfcc so yeah but he is normalizing the c 0 component by the lens yeah then power instead of energy power is energy over lens yeah it is time like you know it is okay like samples or you can see root mean square energy or something yeah yeah do do do do you do okay so but this so 2 things i still do not understand this is supposed to be only for mfcc is and these easy features one question i wanted to ask before does he preserve within the feature vector the framing he is using finally because does he somehow put the information about what is the size about the frame to the feature vector itself or is throwing away this framing no there is no no what you mean like the window length something i did because it does not really it could be good for the recognizance to know what was the chosen framing by this but the thing it is he does not use i guess because at the end like you just you take these features and you try and model so it really matters what frame shift you are using for models like because he needs to use the same frame it might be read for them for the brick and it might screwed up to no why no he will get like every 10 milliseconds every 10 milliseconds you get one feature like it does not really depend on because suppose if you take your case like your window maybe longer so it does not really matter like what size window use or not but at every 10 milliseconds whether you give some features or not really matters no like so see you can use 30 milliseconds or 50 millisecond window 0 probably i did not get it so the framing is still equidistant just the windows are right yeah yeah yeah framing it is really yeah crazy yeah if you change framing it is really it might be difficult i think even it is really problem no like if you try even models also i guess if you change it is not only with this modulation spectra and probably shifts in deltas because maybe it is possible to use it somehow but then there is information about temporary information is somehow included but it is really complicated i think if you use yeah that become really imagine the transformation to get the modulation spectrum out of such a stuff yeah but this is kind of there is also some like not paper but i saw the algorithm i have been using him that called temporal decomposition i do not know if you know if you know that is like atl is temporal deco i do not know who proposed it i just had it from beambot fredego bimbot do you know this guy he is french somewhere now in teleconference like i do not know where it is working but we were using it for a speech coding so we just had a speech and you had decomposed a speech into such segments temporal segments which were stationary inside and then you can more or less quantize those segments somehow and use it for encoding let us say yeah 1st this is proposed but it worked yeah even we read one paper in our rooting group you remember that i have implemented it is quite they were using like svd stuff single the composition for that and it worked pretty well like i was once a lot yeah yeah exactly yeah so why do not you still use it me he was he was quoting well it has been used for speech coding no nobody use it for recognition stuff i think i never heard it yeah but the speech coding so do you still use it no i do not of course why yeah this good question maybe yeah he was quoting that paper also he was telling this this is kind of optimization criteria what this temporal decomposition is doing like you are trying to segment your signal into like discrete windows and then right so but he was telling like this relationship between optimization and then quasi stationality is not really obvious so stationality is again different no stationities yeah that is true this is optimization we want to like see some segments a few segments which can really represent all so maybe that is where like this may not be really that is true yeah this one he is getting some improvement that is important well there should be after each conference there should be something like 10 people should sit down read the papers all the papers them you know all the all the similar papers within one session say focus to this topic and say this is the best this works slightly worse than this one and throw away what is not that good and and i think people would like it many people would like it should should be implemented and used from no no they again i am on the world like forget all the mfcs because it is too old and then starts building the story on the new stuff and but again getting that 10 papers is really difficult task it takes me years again definitely like who will choose the 10 papers i know i know exactly this is just a theory yeah but yeah those things are practically kind of really but i think these kind of things what you said is really good like if people start implementing like some people propose something in feature level some propose in something in model level but these 2 are really independent no one really will combine these 2 are and everybody is like using different training and testing data and then you do not know especially features like suppose if somebody come with different good features again people they show it works for timid but then you try to use some different databases you see it does not work no it is not really sure also suppose people you can not really force people to use same features so they will be happy with their own features and their own scripts so they are bit reluctant to change features every time so a little bit what nist is trying to do now yeah exactly so nist evaluations yeah because to me it seems to everyone is still comparing to plps or mfcs and there are so many new systems and everything is new yeah but nobody is still 20 years old those new systems are still not general like you know like those plp and lmfccs because everybody knows that it works somehow right for any kind of data that is why they are comparing to that so once somebody will come with something new okay he is showing it works for some data but so especially lvcsr and really big systems then people 1st it is difficult to show that it works for all the data is right because yeah but yeah it is again you do not know every paper then should contain all the results from this devolution or some standard task no then there will be only one conference in 3 years or something for speech yeah but then we all optimize on the same task ah yeah yeah so you cannot get anything new after some time but again it could be you would have to publish the results on this standard task and then you could say well i also tried on this and this yeah but then you run out of time that is why you can publish only once in 3 years no but then only it makes some progress no ideally you are correct but i think people are doing suppose in interspeech there was some challenge for speech synthesis so what is like challenge was really good like so they give the database so you have to work on that database what technique uses yeah it is your choice like but the database and then the results analysis is they decide so even speech recognition also some tests are coming like for phone segmentation or something people give some database and then you do this you can use whatever you want and you are to produce even for features or so like can you use from some features also from from the outer world yeah they they design the data such a way that it is it is really like a really real that yeah but they give you also training data no no 1st speech synthesis it is like they give you some data so whether like use for training or you can do like data driven or like model based approach or whatever like at the end then they will ask you to synthesize some sentence and you have to synthesize them and then you have to send them so they give you only the training or development data yeah training data no development training how do they evaluate i just do not know it is again subject to because for speech synthesis they ask people to sit and listen basically yeah listen and yeah yeah even they it is mostly they use native speakers only because they can really judge them okay but they do not have any measure like you have an original speech yeah yeah yeah you transcribe it yeah so even they propose some kind of word error rate so you have the original speech so they synthesize speech is there any of words which are not matching even speech synthesis they also introduced this word wr terms but this was really quite successful in this conference into speech so a lot of people participate right i think even they are continuing this for next year it is just tts right text to speech text to speech right but i heard like even for icasp next year there is some competition by martin krook sheffield and on this feature extraction stuff so at least if you make tasks simple focus then it may be good to compare these features but if you use some large vocabulary system it takes 6 months to build and then at the end you do not know whether your features are really right right so maybe that is like people are always using plps or mfcccs it is such a lot of time involved so you can not really check so maybe well but then i do not know if somebody like nist would have one recognizer and you just plug a different feature but yeah yeah but at least you need we have this numbers recognizers almost kind of freeze so everybody is using right so that is what we are doing no no like we are putting different features and i think yeah numbers recognition is not really different from like at least you need up we have almost same but maybe you are using 29 that is different yeah a couple of people at it here but there are already 2 different setups but at least are they 27 how many they are all using 27 for ogi numbers yeah stories and numbers different setups like some people use this setup and some people that seems we use distinct train and test set even the different sets of one years but the problem is you are using only digits so maybe that is where you are using so maybe that is where you well i do not use only digits my mlps are trained on everything no but test set is digits your main task is digits yeah that is what like it is but oj numbers other like now we are a little bit converged like we are using 27 phones and before it was like 24 25 but it is better like once you have like whatever the back end then you can put features like whether they are gamas or like spectral entropies or mfccs or whatever then at least you can see it would be nice for aurora maybe yeah yeah but aurora is aurora aurora yeah 0 yeah so but aurora is it really big database or how much time it takes to set up system and then no but it is fast it is fast i do not know i have been working with that but the problem is aurora again these models are world based models right yeah so again here we use try phones and so but definitely yeah if you want to show noise i think it is you have to show on aurora for noise conditions yeah it is pretty good arorize real thing those recognizers are made there you just use them that is it you do not have to play with it even it was forbidden in that time right so you have a recognizable just use it you do not play with it we just play with the features yeah yeah but yeah at least i mean those studies i think people already did for even in icslp 2002 special session or features for aurora yeah i remember with david pierce he was this call comics features and ogi features and all this so there are already people but again like at the end like for lvcsr people are using plps or mfcg nothing always who is the one who makes who actually uses what we try to do here these are companies because it is not scientists scientists just start from plp and then design new features new backend or new anything but they start from the old stuff but who is the one who is using these results which we publish and try to make but maybe my idea we are using those results for publishing any other power yeah but once you once you finish your phd you go and but not everybody grow no everybody and those who do not they stay and publish another papers and they turn into life no guillaume you can tell you work with these siemens people you know seamler yeah demler yeah what kind of features they use like they use any you know i do not know you know they are extremely secretive 0 god i had a hard time just to get the signals so i asked other questions but it is quite tough yeah even yeah i did not discuss anything with sunil also like no technical discussions yes these kind of things are built yeah yeah they have to but do you have any feeling that whether they go for all these new features or they usually use only i wonder which level they are using they have yeah they have to do some tests in their mercedes some speech recognition test but it seems now they at least demer they mostly work on dialog management but not much on features and recognition but even they do not believe like with one paper or 2 i guess because at least for them no plps or mfccc they know that okay these things work on every time so okay we can put hands on these things i doubt they are trying right now i doubt that at demler they are trying right now but maybe other companies yeah yeah maybe like yeah because sunil is working in nokia he is a okay so we can hope that nokia will be using tandem but you have you are not sure you mean sunil will not tell us yeah i cannot well many people play with tandem right like mit or who is using that no no no this cohen you mean like i do not know hina told us that somebody is using that very actively like okay it works for them and they are close to like industry like you know there is it is a research group i am not related to i know i do not think it is it is somebody else or i do not know somebody knew us but i but this was sound like at t or what this voice signal company he he nick always mentioned cohen cohen company yeah the word they are using you rodden cohen i do not know he is even if qualcomm no no voice signal they make a rickening is for the samsung phones right right no i do not know but they have like really many recognizers like this yeah i think so maybe definitely they may be using plps or you know maybe they are using plps and but i i believe that there is still some other stuff rasta or whatever else like all those decorrelations and transformations i think it is there but more or less it is again based on mcc is or something like that yeah but whatever is certain to work and they they use it they do not use anything which is too new which is not a new is really different yeah we was like they would at least see at least 44 years of results of some new features and some consistent results it is like a nasa you know they use 486 machines to put to space or space ships because it is known to work perfectly perfectly well or pentium not any p 4 or whatever because it is safe it has been working for 20 years and then only it is true but that is different situation because they need that it does not crash that machine if something happened with a machine and i do not the alux system it does not matter right somebody will fix it and change it but another issue is like again the competition if they want to make on mobile they do not want really use some fancy really expensive competition expensive features so all these i nek also told about this cohen is cell phone which he was showing to work with the lvcsr he was saying that there is something really really simple the decoder i mean not the features but he was mentioning something to the decoder there is just it is just phone recognition or something like even not vitter we just yeah something like no dtw or something i do not know i am not really sure but most of the mobile they use dtw kind of thing all these i do not know so voice calling and this thing 0 yeah yeah yeah that is easy like because they do not really need to put a lot of memory and these things otherwise if they want to build real like but there you could send an sms i think or something like that you could dictate text yeah yeah so even on the phone on the phone on the phone on the phone yeah some new one or the phone that from the cohen go ahead yeah voicing knows that is a similar one to whatever has for example some he was showing it live on mlmi last mlmi you can look at the video but which phone it is it is samsung samsung some new really new for whatever some but some which you can buy right yes yeah but the software is always available you can train it like what do you mean your speech no the software is i do not know that again on top of mobile or like it comes with your mobile well they they just both mobile and based on the os it is using they they know the thing is usually these kind of these kind of facilities are like not not normal so so they may charge more or like yeah yeah it is not the commercial okay yeah definitely they may charge for more money again some extra bill for this using this recognition engine or something or you can just buy that application right i do not know like it is like you you have got some mobile phone and then you can buy from another company some such application to can dictate your sms or whatever i do not know if it is already the case i do not know if it is done now but maybe like if it can not really recognize you will be really but have you ever used like a dictation system for windows those yeah there were many of them like this dragon that was kind of good and i heard that it worked very very well because even when i was working in like in edinburgh like one guy he got some problem with hands and then he was always using this dictation even he used to write a lot of c code with dictation it is really good like so but you have to really try and then you have to kind of your speech yeah okay you had to get used to the system not the system to you yeah yeah it is really like you have to get used to the system like how to say all the call lines and this thing system but it is really funny like he is able to use it for like years and he used to write a lot of code it was really great by the way you should be the one to ask is there anything if i am just a private person and i want for free any software which is able to read me a book in english for example at festival am i able to put the text to the system and to in real time to listen to the yeah you can listen festival is able to to read the minute it is like to read just a normal plain text in reasonable comprehensive english so that i can understand it i think i think i think you can do the festival you can just call like this there are different kind of again how you call the festival so you can just give the full interactive is that there is an interactive so then you will get all the colon and other things if you want to like read full text then you can say like the full text like those syntax and then it will speak till it finishes and it is in real time on a recent animation it is based on the iphone yeah that is why it is really tame otherwise it is a bit difficult yeah it is okay you can easily understand so it is intelligible yeah i think it is very good if it is really good like now it comes with all the linux boxes also because the iphone that i was tried to install it and there are the voices like cat voice and these voices this is not no there are so many voices like again i do not know which which which which voice is running or if it is diphone or which approach this is no mostly the festival what you get on linux missions is diphone based only so they supply a few voices of the iphones yeah yeah just you can not you got it installed in your machine here no it is all i just put like a festival right because it comes with direct linux software only now it is open source and have you tried it yes i have tried and there are some built in like easy voices which you can there is a default one completely synthetic which you cannot understand at all then there is a diphone synthesis i think it is the diphone synthesis from the something like cat voice yeah cat voice like they are like they have so so but this does not sound very nice and we were playing at the summer school with them actually with the authors and they were playing us some other yeah new voices like yeah and it sounded like human it was great yeah that is the difference that in cstir views this unit selection it is not diphones you have really large inventory of the speech so this is not those voices they are not released or something no no it is free they will be releasing soon i guess but maybe you can just see yours so far no it is it is here on the web then it will not come with your linux software so you are to again download from okay i am fine with this but i can yeah you can download make it run on my machine yeah if the voices are called multi syn okay multi sin some voice so you can download from the voice from the web then the engine is in the festival yeah yeah yeah so it is just a voice yeah yeah okay so voice is like the database of the speaker who speaks this takes home if it is really large database so the quality will be better because you can find similar still real time yeah it is more or less but not exactly like no okay it is 2 times or one4 times or but not much but you can prove like how much you want because okay so this is again like that is kind of my phd work how in this system like like how you choose the units and how you concatenate so there are again cost functions and vitterby yeah it is right you can always optimize you can put some thresholds and pruning then you can make it real time so then again compromise between the quality and yeah but still it is good like now you can just check multi syn and then voices that is right yeah it is really good and you just have chosen some text any text you just put it in i use the douglas adams that book you know okay but only sometimes like it can not like all this general english text book yeah i try and it can read how does it read from which how to stay in it is it possible to read pdfs or just standard text no txt whatever txt right yeah ascii ask a file at least you can take a pdf and convert pdf to us but only the problem is again acronyms and sometimes it expands sometimes it may not expand because it was not in their dictionary so it is not in the dictionary like if again it is problem like so but those things you can add like if you are really familiar with festival so you can add always these i am not and i do not want to go too much in the details but at least these things okay like it will not really make mistakes so often like sometimes anyway who is the author of festival ellen black and paul taylor they started a festival in 96 these are the edinburgh people edinburgh cs 2 and i thought that is the guy from cslu from portland who was there this this johan woters yeah but there was no they did portland ogi they did lpc based synthesis the festival is already like it is 11st we start with no but that was the guy who is on a wheelchair now he cannot move even he is very in like in the say that like cannot he just can speak only through some festival system or something like that from what you no yeah he was working my thing in oji even he is very famous but i said that he was the author no it was developed for him like he was just 1st tester i think of the so okay okay something like yeah maybe because it is not text to speech it is just the synthesis which means which is more or less the same but i do not know yeah 1st it started in cstr with telen black and paul taylor yeah then it takes into many places because if somebody is and simon king is playing with that right no simon king is one of the others but rob clark is the main idea you made yeah rob voice rob voice is there okay that i know yeah i think that is enough so anything more about lisbon or interest people no other papers further just one paper and festival from linux it is very interesting okay okay yeah yeah yeah yeah   you not one this too that is a good one size this is great stuff yeah like this paper maybe it is not easy to name this room without breaking anything