We propose a clustering based adaptive Sequence-to-Sequence model to improve the performance of dialogue systems . Different with previous models who treat all the dialogue data as input of a single model , we cluster the dialogue data and use several Sequence-to-Sequence models to train different cluster of data to catch different characteristic in different cluster . Our experiments show that our models can improve the performance of dialogue systems . This can remove the manual selection of reference audios process and makes prosody modelling an end-to-end procedure . Experimental analysis shows that GraphTTS outperforms the state-of-the-art sequence-to-sequence models by 0.24 in Mean Opinion Score ( MOS ) . GAE can adjust the pause , ventilation and tones of synthesised audios automatically . section : Related Work Machine translation using deep neural networks achieved great success with sequence - to - sequence models that used recurrent neural networks ( RNNs ) with long short - term memory ( LSTM , ) cells . The basic sequence - to - sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed - sized state vector . Surprisingly , attention degree for the single word expression like ” stone ” , ” wall ” and ” leaves ” is lower to compare with multiword phrases . Sequence models lack this property as they have no explicit composition module to produce such mutiword phrases . Finally , the most interesting pattern is that the model attends over higher level ( low depth ) tree nodes with rich semantics when considering a ( c ) longer phrase or ( d ) full sentence . Figure 1 shows a simple example from the popular dataset SQuAD . Many significant works are based on this task , and most of them focus on the improvement of a sequence model that is augmented with an attention mechanism . However , the encoding of the words is also crucial and a better encoding layer can lead to substantial difference to the final performance . section : Related Work Sequence models are typically trained to predict the next word using the cross - entropy loss . At test time , it is common to use beam search to explore multiple alternative paths sutskever2014 , bahdanau - iclr2015 , rush - 2015 . More concretely , our approach utilizes the off-the-shelf confusionset for guiding the character generation . To this end , our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network , or generate a character from the confusionset rather than the entire vocabulary . We conduct experiments on three human-annotated datasets , and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20 % F1 score , achieving state-of-the-art performance on three datasets . In this method , the multiobjective optimization problem is decomposed to a number of single-objective optimization subproblems and all the subproblems are optimized in a collaborative manner . Each subproblem is modeled with a pointer network and the model is trained with reinforcement learning . However , when pointer network extracts the features of an instance , it ignores the underlying structure information of the input nodes . Owing to the development of multi-modal natural language interaction , Bots no longer rely on the graphical desktop for interaction . Consequently , a new interface paradigm is required to normalize the bots-based human-computer interaction , which is no longer limited to the WIMP ( windows , icon , menu , and pointer ) paradigm of the desktop paradigm . In this paper , a new ASLI ( avatar , scenario , language , and instrument ) interface paradigm is proposed on the basis of the extended avatar metaphor . User interfaces have traditionally followed the WIMP ( window , icon , menu , pointer ) paradigm . Though functional and powerful , they are usually cumbersome for a novice user to design a complex model , requiring considerable expertise and effort . However , hindered by the intrinsic restriction of the underlying neural network design , such schedulers often suffer from poor flexibility that may induce resource under- utilization , or overly fine-grained control that induces considerable overheads . In this paper , we address the above challenges by integrating pointer network architecture with the scheduler design , and proposing Neural Task Scheduling ( NTS ) , an online flexible task scheduling scheme which effectively reduces average task slowdown to facilitate best quality-of-service . Simulation results show that NTS consistently outperforms state-of-the-art schemes under different settings . This work provides the first neural network-based approach to argumentation mining , focusing on the two tasks of extracting links between argument components , and classifying types of argument components . In order to solve this problem , we propose to use a joint model that is based on a Pointer Network architecture . A Pointer Network is appealing for this task for the following reasons : 1 ) It takes into account the sequential nature of argument components ; 2 ) By construction , it enforces certain properties of the tree structure present in argument relations ; 3 ) The hidden representations can be applied to auxiliary tasks . With the utilization of the structural characteristics of combinatorial optimization problems and the ability to extract the rule behind the data by deep learning , pointer network has been successfully applied to solve several classical combinatorial optimization problems . In this paper , a pointer network based algorithm is designed to solve UBQP problems . The network model is trained by supervised learning ( SL ) and deep reinforcement learning ( DRL ) respectively . It uses a lock-free creation and asynchronous commitment mechanism to decouple the relationship between threads , hence reduce the overhead of locks . ( 2 ) A pointer-based communication algorithm was proposed to improve efficiency of communication between threads . It uses a buffer mechanism to avoid interrupting the execution of target thread . The invention discloses a self-calibration method of pointer graph computer through a known computer program instrument in the computer , which comprises the following parts : computer image , gathering card , cam and pointer graph self-calibration program , wherein the self-reading program is loaded on the hard disk of computer , which prompts the location through locating mark ; the dial image is read by image gathering card in the computer internal memory , which fits pointer position through distant least square method after the image is functioned by two-valued ; the pointer graph reads the number and generates the test report . The invention improves the efficiency and precision of instrument calibration , which can calibrate the instrument and read the system automatically . This paper studies the compositional definition and behaviour of properties that arise in pointer structures . A pointer structure is represented as a ( pointer ) graph . A pointer property is a set of pointer structures . A data monitor apparatus includes a comparator circuit ( 1 ) for comparing received data and reference data ; a recorder circuit ( 3 ) for recording an output from said comparator circuit ( 1 ) ; a pointer control circuit ( 4 ) composed of a first counter ( 13A ) responsive to a comparison result of the comparator circuit ( 1 ) , a first setting unit ( 12A ) for setting a remaining count value of the first counter ( 13A ) , a second counter ( 13B ) responsive to a comparison result of the comparator circuit ( 1 ) , a second setting unit (12)B ) for setting a remaining count value of the second counter ( 13B ) ; a selector ( 11 ) for accepting the received data as an input ; a first pointer ( 14A ) responsive to an output from the selector ( 11 ) , and a second pointer ( 14B ) responsive to an output from the selector ( 11 ) ; and a compressed data recorder circuit ( 5 ) composed of a first recorder circuit ( 15A ) for recording the received data with use of the first pointer ( 14A ) and a second recorder circuit ( 15B ) for recording the received data with use of the second pointer ( 14B ) . With such arrangement , the received data and reference data are compared , and if there is any abnormality , only data in the vicinity of a portion of the data having the abnormality is recorded , whereby a troubled portion can be found with ease from the recorded data . The existing relevance-based , popularity-based and regular combinatorial strategies fail to take the internal or external competitions among keywords into consideration . In this paper , we regard keyword recommendations as a combinatorial optimization problem and solve it with a modified pointer network structure . The model is trained on an actor-critic based deep reinforcement learning framework . It is shown first that the use of the pointer introduced in SDH is adequate . A pointer scheme with a fourmultiframe structure is proposed , aiming at efficient information transmission . The characteristics of the fourmultiframe-type pointer are then discussed , and the assignment of the overhead required in the transfer of maintenance/operation information is considered . section : RELATED WORK Since their introduction in [ reference ] , many improvements have been proposed for RNNLMs , including different dropout methods [ reference ][ reference ] , novel recurrent units [ reference ] , and use of pointer networks to complement the recurrent neural network [ reference ] . However , none of the improvements dealt with the loss structure , and to the best of our knowledge , our work is the first to offer a new loss framework . In summarization , an intuitive way to handle such OOV words is to simply point to their location in the source document instead . We model this notion using our novel switching decoder / pointer architecture which is graphically represented in Figure [ reference ] . In this model , the decoder is equipped with a ‘ switch ’ that decides between using the generator or a pointer at every time - step . Clearly , capturing the ‘ meaning ’ of complex sentences remains a weakness of these models . Our next example output , presented in Figure [ reference ] , displays the sample output from the switching generator / pointer model on the Gigaword corpus . It is apparent from the examples that the model learns to use pointers very accurately not only for named entities , but also for multi - word phrases . In this paper , we propose a semi-supervised training framework to improve the data efficiency of Tacotron . The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large , publicly-available text and speech corpora . Importantly , these external data are unpaired and potentially noisy . Speech synthesis for Tibetan Lhasa dialect is implemented on the basis of an end-to-end novel speech synthesis framework , Tacotron . The training transcript has used the phoneme list transcribed from Tibetan characters , and feature parameters were extracted from the mel-spectrogram . BERT representations learned from large amounts of unlabeled text data are shown to contain very rich semantic and syntactic information about the input text , and have potential to be leveraged by a TTS system to compensate the lack of high-quality data . We incorporate BERT as a parallel branch to the Tacotron-2 encoder with its own attention head . For an input text , it is simultaneously passed into BERT and the Tacotron-2 encoder . We investigate a novel cross-lingual multi-speaker text-to-speech synthesis approach for generating high-quality native or accented speech for native/foreign seen/unseen speakers in English and Mandarin . The system consists of three separately trained components : an x-vector speaker encoder , a Tacotron-based synthesizer and a WaveNet vocoder . It is conditioned on 3 kinds of embeddings : ( 1 ) speaker embedding so that the system can be trained with speech from many speakers will little data from each speaker ; ( 2 ) language embedding with shared phoneme inputs ; ( 3 ) stress and tone embedding which improves naturalness of synthesized speech , especially for a tonal language like Mandarin . In this paper , multi speaker speech synthesis using speaker embedding is proposed . The proposed model is based on Tacotron network , but post-processing network of the model is modified with dilated convolution layers , which used in Wavenet architecture , to make it more adaptive to speech . The model can generate multi speaker voice with only one neural network model by giving auxiliary input data , speaker embedding , to the network . This paper presents ByteSing , a Chinese singing voice synthesis ( SVS ) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders . Different from the conventional SVS models , the proposed ByteSing employs Tacotron-like encoder-decoder structures as the acoustic models , in which the CBHG models and recurrent neural networks ( RNNs ) are explored as encoders and decoders respectively . However , to introduce Tacotron into Mandarin Chinese Text-to-Speech ( TTS ) , a prosody system is needed for generating more natural speech . This paper proposes a practical method to involve the prosodic annotation into Tacotron training for Mandarin Chinese synthesis system . A prosody model predicting the prosodic boundaries from the given text serves as the front-end system in our approach , followed by a Tacotron synthesis system trained with well-labeled TTS database containing the prosodic annotations . Most current prosody preserving VC systems suffer from target similarity and quality issues in the converted speech . To address these problems , we first recover initial prosody preserving speech using a Phonetic Posteriorgrams ( PPGs ) based Tacotron synthesizer . Then , we enhance the quality of the converted speech using a novel speech-enhancement network , which is based on a combination of phoneme recognition and Tacotron networks . Detailed annotation of FPs in training data enables precise control at synthesis time ; coarse or nonexistent FP annotation , when combined with stochastic attention-based neural TTS , leads to synthesisers that insert these phenomena into fluent prompts on their own accord . In this study we investigate , objectively and subjectively , the effects of FP annotation and the impact of relinquishing control over FPs in a Tacotron TTS system . The training corpus comprised 9 hours of singlespeaker breath groups extracted from a conversational podcast . On the other hand , the conventional pipeline models are broadly used in practical TTS systems since there are few crucial prediction errors in the duration model . For realizing high-quality practical TTS systems without attention prediction errors , this paper investigates Tacotron-based acoustic models with phoneme alignment instead of attention . The phoneme durations are first obtained from HMM-based forced alignment and the duration model is a simple bidirectional LSTM-based network . Text-to-speech synthesis is a promising human- computer interaction technology . Google launched the TTS model Tacotron , which can directly convert raw text to speech . The encoder module is one of the most important components of Tacotron . document : Natural TTS Synthesis By Conditioning WaveNet On Mel Spectrogram Predictions This paper describes Tacotron 2 , a neural network architecture for speech synthesis directly from text . The system is composed of a recurrent sequence - to - sequence feature prediction network that maps character embeddings to mel - scale spectrograms , followed by a modified WaveNet model acting as a vocoder to synthesize time - domain waveforms from those spectrograms . The inputs to WaveNet ( linguistic features , predicted log fundamental frequency ( ) , and phoneme durations ) , however , require significant domain expertise to produce , involving elaborate text - analysis systems as well as a robust lexicon ( pronunciation guide ) . Tacotron , a sequence - to - sequence architecture for producing magnitude spectrograms from a sequence of characters , simplifies the traditional speech synthesis pipeline by replacing the production of these linguistic and acoustic features with a single neural network trained from data alone . To vocode the resulting magnitude spectrograms , Tacotron uses the Griffin - Lim algorithm for phase estimation , followed by an inverse short - time Fourier transform . To measure the performance of GAN-TTS , we employ both subjective human evaluation ( MOS - Mean Opinion Score ) , as well as novel quantitative metrics ( Fr\'echet DeepSpeech Distance and Kernel DeepSpeech Distance ) , which we find to be well correlated with MOS . We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models , and unlike autoregressive models , it is highly parallelisable thanks to an efficient feed-forward generator . Listen to GAN-TTS reading this abstract at this https URL
We propose a clustering based adaptive Sequence-to-Sequence model to improve the performance of dialogue systems . Different with previous models who treat all the dialogue data as input of a single model , we cluster the dialogue data and use several Sequence-to-Sequence models to train different cluster of data to catch different characteristic in different cluster . Our experiments show that our models can improve the performance of dialogue systems .
This can remove the manual selection of reference audios process and makes prosody modelling an end-to-end procedure . Experimental analysis shows that GraphTTS outperforms the state-of-the-art sequence-to-sequence models by 0.24 in Mean Opinion Score ( MOS ) . GAE can adjust the pause , ventilation and tones of synthesised audios automatically .
section : Related Work Machine translation using deep neural networks achieved great success with sequence - to - sequence models that used recurrent neural networks ( RNNs ) with long short - term memory ( LSTM , ) cells . The basic sequence - to - sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed - sized state vector .
Surprisingly , attention degree for the single word expression like ” stone ” , ” wall ” and ” leaves ” is lower to compare with multiword phrases . Sequence models lack this property as they have no explicit composition module to produce such mutiword phrases . Finally , the most interesting pattern is that the model attends over higher level ( low depth ) tree nodes with rich semantics when considering a ( c ) longer phrase or ( d ) full sentence .
Figure 1 shows a simple example from the popular dataset SQuAD . Many significant works are based on this task , and most of them focus on the improvement of a sequence model that is augmented with an attention mechanism . However , the encoding of the words is also crucial and a better encoding layer can lead to substantial difference to the final performance .
section : Related Work Sequence models are typically trained to predict the next word using the cross - entropy loss . At test time , it is common to use beam search to explore multiple alternative paths sutskever2014 , bahdanau - iclr2015 , rush - 2015 .
More concretely , our approach utilizes the off-the-shelf confusionset for guiding the character generation . To this end , our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network , or generate a character from the confusionset rather than the entire vocabulary . We conduct experiments on three human-annotated datasets , and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20 % F1 score , achieving state-of-the-art performance on three datasets .
In this method , the multiobjective optimization problem is decomposed to a number of single-objective optimization subproblems and all the subproblems are optimized in a collaborative manner . Each subproblem is modeled with a pointer network and the model is trained with reinforcement learning . However , when pointer network extracts the features of an instance , it ignores the underlying structure information of the input nodes .
Owing to the development of multi-modal natural language interaction , Bots no longer rely on the graphical desktop for interaction . Consequently , a new interface paradigm is required to normalize the bots-based human-computer interaction , which is no longer limited to the WIMP ( windows , icon , menu , and pointer ) paradigm of the desktop paradigm . In this paper , a new ASLI ( avatar , scenario , language , and instrument ) interface paradigm is proposed on the basis of the extended avatar metaphor .
User interfaces have traditionally followed the WIMP ( window , icon , menu , pointer ) paradigm . Though functional and powerful , they are usually cumbersome for a novice user to design a complex model , requiring considerable expertise and effort .
However , hindered by the intrinsic restriction of the underlying neural network design , such schedulers often suffer from poor flexibility that may induce resource under- utilization , or overly fine-grained control that induces considerable overheads . In this paper , we address the above challenges by integrating pointer network architecture with the scheduler design , and proposing Neural Task Scheduling ( NTS ) , an online flexible task scheduling scheme which effectively reduces average task slowdown to facilitate best quality-of-service . Simulation results show that NTS consistently outperforms state-of-the-art schemes under different settings .
This work provides the first neural network-based approach to argumentation mining , focusing on the two tasks of extracting links between argument components , and classifying types of argument components . In order to solve this problem , we propose to use a joint model that is based on a Pointer Network architecture . A Pointer Network is appealing for this task for the following reasons : 1 ) It takes into account the sequential nature of argument components ; 2 ) By construction , it enforces certain properties of the tree structure present in argument relations ; 3 ) The hidden representations can be applied to auxiliary tasks .
With the utilization of the structural characteristics of combinatorial optimization problems and the ability to extract the rule behind the data by deep learning , pointer network has been successfully applied to solve several classical combinatorial optimization problems . In this paper , a pointer network based algorithm is designed to solve UBQP problems . The network model is trained by supervised learning ( SL ) and deep reinforcement learning ( DRL ) respectively .
It uses a lock-free creation and asynchronous commitment mechanism to decouple the relationship between threads , hence reduce the overhead of locks . ( 2 ) A pointer-based communication algorithm was proposed to improve efficiency of communication between threads . It uses a buffer mechanism to avoid interrupting the execution of target thread .
The invention discloses a self-calibration method of pointer graph computer through a known computer program instrument in the computer , which comprises the following parts : computer image , gathering card , cam and pointer graph self-calibration program , wherein the self-reading program is loaded on the hard disk of computer , which prompts the location through locating mark ; the dial image is read by image gathering card in the computer internal memory , which fits pointer position through distant least square method after the image is functioned by two-valued ; the pointer graph reads the number and generates the test report . The invention improves the efficiency and precision of instrument calibration , which can calibrate the instrument and read the system automatically .
This paper studies the compositional definition and behaviour of properties that arise in pointer structures . A pointer structure is represented as a ( pointer ) graph . A pointer property is a set of pointer structures .
A data monitor apparatus includes a comparator circuit ( 1 ) for comparing received data and reference data ; a recorder circuit ( 3 ) for recording an output from said comparator circuit ( 1 ) ; a pointer control circuit ( 4 ) composed of a first counter ( 13A ) responsive to a comparison result of the comparator circuit ( 1 ) , a first setting unit ( 12A ) for setting a remaining count value of the first counter ( 13A ) , a second counter ( 13B ) responsive to a comparison result of the comparator circuit ( 1 ) , a second setting unit (12)B ) for setting a remaining count value of the second counter ( 13B ) ; a selector ( 11 ) for accepting the received data as an input ; a first pointer ( 14A ) responsive to an output from the selector ( 11 ) , and a second pointer ( 14B ) responsive to an output from the selector ( 11 ) ; and a compressed data recorder circuit ( 5 ) composed of a first recorder circuit ( 15A ) for recording the received data with use of the first pointer ( 14A ) and a second recorder circuit ( 15B ) for recording the received data with use of the second pointer ( 14B ) . With such arrangement , the received data and reference data are compared , and if there is any abnormality , only data in the vicinity of a portion of the data having the abnormality is recorded , whereby a troubled portion can be found with ease from the recorded data .
The existing relevance-based , popularity-based and regular combinatorial strategies fail to take the internal or external competitions among keywords into consideration . In this paper , we regard keyword recommendations as a combinatorial optimization problem and solve it with a modified pointer network structure . The model is trained on an actor-critic based deep reinforcement learning framework .
It is shown first that the use of the pointer introduced in SDH is adequate . A pointer scheme with a fourmultiframe structure is proposed , aiming at efficient information transmission . The characteristics of the fourmultiframe-type pointer are then discussed , and the assignment of the overhead required in the transfer of maintenance/operation information is considered .
section : RELATED WORK Since their introduction in [ reference ] , many improvements have been proposed for RNNLMs , including different dropout methods [ reference ][ reference ] , novel recurrent units [ reference ] , and use of pointer networks to complement the recurrent neural network [ reference ] . However , none of the improvements dealt with the loss structure , and to the best of our knowledge , our work is the first to offer a new loss framework .
In summarization , an intuitive way to handle such OOV words is to simply point to their location in the source document instead . We model this notion using our novel switching decoder / pointer architecture which is graphically represented in Figure [ reference ] . In this model , the decoder is equipped with a ‘ switch ’ that decides between using the generator or a pointer at every time - step .
Clearly , capturing the ‘ meaning ’ of complex sentences remains a weakness of these models . Our next example output , presented in Figure [ reference ] , displays the sample output from the switching generator / pointer model on the Gigaword corpus . It is apparent from the examples that the model learns to use pointers very accurately not only for named entities , but also for multi - word phrases .
In this paper , we propose a semi-supervised training framework to improve the data efficiency of Tacotron . The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large , publicly-available text and speech corpora . Importantly , these external data are unpaired and potentially noisy .
Speech synthesis for Tibetan Lhasa dialect is implemented on the basis of an end-to-end novel speech synthesis framework , Tacotron . The training transcript has used the phoneme list transcribed from Tibetan characters , and feature parameters were extracted from the mel-spectrogram .
BERT representations learned from large amounts of unlabeled text data are shown to contain very rich semantic and syntactic information about the input text , and have potential to be leveraged by a TTS system to compensate the lack of high-quality data . We incorporate BERT as a parallel branch to the Tacotron-2 encoder with its own attention head . For an input text , it is simultaneously passed into BERT and the Tacotron-2 encoder .
We investigate a novel cross-lingual multi-speaker text-to-speech synthesis approach for generating high-quality native or accented speech for native/foreign seen/unseen speakers in English and Mandarin . The system consists of three separately trained components : an x-vector speaker encoder , a Tacotron-based synthesizer and a WaveNet vocoder . It is conditioned on 3 kinds of embeddings : ( 1 ) speaker embedding so that the system can be trained with speech from many speakers will little data from each speaker ; ( 2 ) language embedding with shared phoneme inputs ; ( 3 ) stress and tone embedding which improves naturalness of synthesized speech , especially for a tonal language like Mandarin .
In this paper , multi speaker speech synthesis using speaker embedding is proposed . The proposed model is based on Tacotron network , but post-processing network of the model is modified with dilated convolution layers , which used in Wavenet architecture , to make it more adaptive to speech . The model can generate multi speaker voice with only one neural network model by giving auxiliary input data , speaker embedding , to the network .
This paper presents ByteSing , a Chinese singing voice synthesis ( SVS ) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders . Different from the conventional SVS models , the proposed ByteSing employs Tacotron-like encoder-decoder structures as the acoustic models , in which the CBHG models and recurrent neural networks ( RNNs ) are explored as encoders and decoders respectively .
However , to introduce Tacotron into Mandarin Chinese Text-to-Speech ( TTS ) , a prosody system is needed for generating more natural speech . This paper proposes a practical method to involve the prosodic annotation into Tacotron training for Mandarin Chinese synthesis system . A prosody model predicting the prosodic boundaries from the given text serves as the front-end system in our approach , followed by a Tacotron synthesis system trained with well-labeled TTS database containing the prosodic annotations .
Most current prosody preserving VC systems suffer from target similarity and quality issues in the converted speech . To address these problems , we first recover initial prosody preserving speech using a Phonetic Posteriorgrams ( PPGs ) based Tacotron synthesizer . Then , we enhance the quality of the converted speech using a novel speech-enhancement network , which is based on a combination of phoneme recognition and Tacotron networks .
Detailed annotation of FPs in training data enables precise control at synthesis time ; coarse or nonexistent FP annotation , when combined with stochastic attention-based neural TTS , leads to synthesisers that insert these phenomena into fluent prompts on their own accord . In this study we investigate , objectively and subjectively , the effects of FP annotation and the impact of relinquishing control over FPs in a Tacotron TTS system . The training corpus comprised 9 hours of singlespeaker breath groups extracted from a conversational podcast .
On the other hand , the conventional pipeline models are broadly used in practical TTS systems since there are few crucial prediction errors in the duration model . For realizing high-quality practical TTS systems without attention prediction errors , this paper investigates Tacotron-based acoustic models with phoneme alignment instead of attention . The phoneme durations are first obtained from HMM-based forced alignment and the duration model is a simple bidirectional LSTM-based network .
Text-to-speech synthesis is a promising human- computer interaction technology . Google launched the TTS model Tacotron , which can directly convert raw text to speech . The encoder module is one of the most important components of Tacotron .
document : Natural TTS Synthesis By Conditioning WaveNet On Mel Spectrogram Predictions This paper describes Tacotron 2 , a neural network architecture for speech synthesis directly from text . The system is composed of a recurrent sequence - to - sequence feature prediction network that maps character embeddings to mel - scale spectrograms , followed by a modified WaveNet model acting as a vocoder to synthesize time - domain waveforms from those spectrograms .
The inputs to WaveNet ( linguistic features , predicted log fundamental frequency ( ) , and phoneme durations ) , however , require significant domain expertise to produce , involving elaborate text - analysis systems as well as a robust lexicon ( pronunciation guide ) . Tacotron , a sequence - to - sequence architecture for producing magnitude spectrograms from a sequence of characters , simplifies the traditional speech synthesis pipeline by replacing the production of these linguistic and acoustic features with a single neural network trained from data alone . To vocode the resulting magnitude spectrograms , Tacotron uses the Griffin - Lim algorithm for phase estimation , followed by an inverse short - time Fourier transform .
To measure the performance of GAN-TTS , we employ both subjective human evaluation ( MOS - Mean Opinion Score ) , as well as novel quantitative metrics ( Fr\'echet DeepSpeech Distance and Kernel DeepSpeech Distance ) , which we find to be well correlated with MOS . We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models , and unlike autoregressive models , it is highly parallelisable thanks to an efficient feed-forward generator . Listen to GAN-TTS reading this abstract at this https URL
A Clustering Based Adaptive Sequence-to-Sequence Model for Dialogue Systems GraphTTS: Graph-to-Sequence Modelling in Neural Text-to-Speech Depthwise Separable Convolutions for Neural Machine Translation Neural Tree Indexers for Text Understanding MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Sequence Level Training with Recurrent Neural Networks Confusionset-guided Pointer Networks for Chinese Spelling Check MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization Interface paradigm of Bots-based human-computer interaction Sketch-based design for green geometry and image deformation Neural Task Scheduling with Reinforcement Learning for Fog Computing Systems Here's My Point: Joint Pointer Architecture for Argument Mining A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem A hierarchical parallel discrete event simulation kernel for multicore platform Automatic reading method for analog meter Compositional Reasoning for Pointer Structures Controller of data Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning A method for transporting sdh signals using existing transmission systems Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Semi-supervised Training for Improving Data Efficiency in End-to-end Speech Synthesis End-to-end Speech Synthesis for Tibetan Lhasa Dialect Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models Cross-lingual Multi-speaker Text-to-speech Synthesis for Voice Cloning without Using Parallel Corpus for Unseen Speakers DNN based multi-speaker speech synthesis with temporal auxiliary speaker ID embedding ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders A Prosodic Mandarin Text-to-Speech System Based on Tacotron Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data How to train your fillers: uh and um in spontaneous speech synthesis Tacotron-Based Acoustic Model Using Phoneme Alignment for Practical Neural Text-to-Speech Systems A Hardware-efficient Accelerator for Encoding Stage of Text-to-speech Synthesis Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions High Fidelity Speech Synthesis with Adversarial Networks
8db7bf98ac33abb1d1701bf5d9c45aca9449397a bba993f5a86ad39d1e45e6880aa605e018a52679 c07181117ef987628ca2895a576db29c4b9221cc eb5a9a5e023c894111753542827dc7431ff6f3fb 074f4cb2a330db59ded2602044603cd73fe91c0a cf15ea7913281657130cdb0988dd59fff6401e12 7f98f48327fb27f5e903abdc7fbe699fb488bbd8 0cf565a68460e6ff590a406eb907b229d6bc8fc6 2a83ccbbaec59f6cc73278c39fcca73a4dfbcdc6 d53fa9f994c7c267a182e115c7794dc0b8836c68 d50b97dab2f504dfcd849cb138f501f7453ccdae b2ced621a9575cc9e7dd2c6675140b619b8e2835 30aeaf77e34b8618146dc5b2209e97966d65964b dd2974df96d2d623f402a352eff22731062d360b 66e7d8d18e1c429205b6689eaafa85eac134fb8b 728938faa28719e22a9de5ce87717180196da01b 489c3ebe0959195a5a37cdac4a78870382f60e43 af9966927f5612b9c79ae6a1efeb556ff569f73f 4d632aa4fe405b7cb7efa4d07d6e79f9afcf167d ae2ba00e9bdabdebe1df92cc4d5829d2cbb55532 bcafd07960446e5173d78853ab3c2eeb50233d38 5b426c119422011f434e5f7920616fe3aae966df f5d1d09e88a498dca7cca826e39d7a2c05a0b2e2 e0815bc0c2e55a4f3b88be0cc4a1c9583b2d2392 8d43f9375fedca8739cd810fa9d723992f118abe 3b243d52c40aa3c5ca9a569e209aeaaada765874 85381e0e89e98ea1ed8bd3dfa533d911263757df
["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"]
2018 2020 2018 2017 2017 2016 2019 2020 2018 2013 2019 2017 2020 2013 2004 2006 1986 2019 1991 2017 2016 2019 2019 2019 2019 2019 2020 2019 2019 2019 2019 2019 2018 2019
BigComp ICASSP ICLR EACL ArXiv ICLR ACL (1) GLOBECOM EMNLP MPC ICLR CoNLL ICASSP ICEIC APSIPA ASRU ASICON 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
https://api.semanticscholar.org/8db7bf98ac33abb1d1701bf5d9c45aca9449397a https://api.semanticscholar.org/bba993f5a86ad39d1e45e6880aa605e018a52679 https://www.semanticscholar.org/paper/07c4fc48ad7b7d1a417b0bb72d0ae2d4efc5aa83 https://www.semanticscholar.org/paper/705dcc8eadba137834e4b0359e2d696d4b209f5b https://www.semanticscholar.org/paper/12e20e4ea572dbe476fd894c5c9a9930cf250dd2 https://www.semanticscholar.org/paper/35c1668dc64d24a28c6041978e5fcca754eb2f4b https://api.semanticscholar.org/c07181117ef987628ca2895a576db29c4b9221cc https://api.semanticscholar.org/eb5a9a5e023c894111753542827dc7431ff6f3fb https://api.semanticscholar.org/074f4cb2a330db59ded2602044603cd73fe91c0a https://api.semanticscholar.org/cf15ea7913281657130cdb0988dd59fff6401e12 https://api.semanticscholar.org/7f98f48327fb27f5e903abdc7fbe699fb488bbd8 https://api.semanticscholar.org/0cf565a68460e6ff590a406eb907b229d6bc8fc6 https://api.semanticscholar.org/2a83ccbbaec59f6cc73278c39fcca73a4dfbcdc6 https://api.semanticscholar.org/d53fa9f994c7c267a182e115c7794dc0b8836c68 https://api.semanticscholar.org/d50b97dab2f504dfcd849cb138f501f7453ccdae https://api.semanticscholar.org/b2ced621a9575cc9e7dd2c6675140b619b8e2835 https://api.semanticscholar.org/30aeaf77e34b8618146dc5b2209e97966d65964b https://api.semanticscholar.org/dd2974df96d2d623f402a352eff22731062d360b https://api.semanticscholar.org/66e7d8d18e1c429205b6689eaafa85eac134fb8b https://www.semanticscholar.org/paper/424aef7340ee618132cc3314669400e23ad910ba https://www.semanticscholar.org/paper/f37076f426023241f19cdc2fb0a0fd733a6fa7fa https://api.semanticscholar.org/728938faa28719e22a9de5ce87717180196da01b https://api.semanticscholar.org/489c3ebe0959195a5a37cdac4a78870382f60e43 https://api.semanticscholar.org/af9966927f5612b9c79ae6a1efeb556ff569f73f https://api.semanticscholar.org/4d632aa4fe405b7cb7efa4d07d6e79f9afcf167d https://api.semanticscholar.org/ae2ba00e9bdabdebe1df92cc4d5829d2cbb55532 https://api.semanticscholar.org/bcafd07960446e5173d78853ab3c2eeb50233d38 https://api.semanticscholar.org/5b426c119422011f434e5f7920616fe3aae966df https://api.semanticscholar.org/f5d1d09e88a498dca7cca826e39d7a2c05a0b2e2 https://api.semanticscholar.org/e0815bc0c2e55a4f3b88be0cc4a1c9583b2d2392 https://api.semanticscholar.org/8d43f9375fedca8739cd810fa9d723992f118abe https://api.semanticscholar.org/3b243d52c40aa3c5ca9a569e209aeaaada765874 https://www.semanticscholar.org/paper/1a2599e467e855f845dcbf9282f8bdbd97b85708 https://api.semanticscholar.org/85381e0e89e98ea1ed8bd3dfa533d911263757df