Attention based end-to-end speech synthesis achieves better performance in both prosody and quality compared to the conventional " front-end"-"back-end " structure . But training such end-to-end framework is usually time-consuming because of the use of recurrent neural networks . To enable parallel calculation and long-range dependency modeling , a solely self-attention based framework named Transformer is proposed recently in the end-to-end family . The paper provides an overview of modern recurrent neural network architectures that can be used as a model ( building tool ) for diagnosis and predicting of infocommunication systems elements . The paper presents arguments in favor of the using of recurrent neural networks . Insufficient level of geographic information in nucleotide sequence repositories such as GenBank motivates the use of natural language processing methods for extracting geographic location names ( toponyms ) in the scientific article associated with the sequence , and disambiguating the locations to their co-ordinates . In this paper , we present an extensive study of multiple recurrent neural network architectures for the task of extracting geographic locations and their effective contribution to the disambiguation task using population heuristics . The methods presented in this paper achieve a strict detection F1 score of 0.94 , disambiguation accuracy of 91 % and an overall resolution F1 score of 0.88 that are significantly higher than previously developed methods , improving our capability to find the location of infected hosts and enrich metadata information . DCR is able to predict answers of variable lengths , whereas previous neural RC models primarily focused on predicting single tokens or entities . DCR encodes a document and an input question with recurrent neural networks , and then applies a word - by - word attention mechanism to acquire question - aware representations for the document , followed by the generation of chunk representations and a ranking module to propose the top - ranked chunk as the answer . Experimental results show that DCR achieves state - of - the - art exact match and F1 scores on the SQuAD dataset . Natural language processing tasks such as machine translation and textual entailment are concerned with modeling two sequences rather than a single one . A standard tool for modeling two sequences with recurrent networks is the encoder - decoder architecture where the second sequence ( also known as the target ) is being processed conditioned on the first one ( also known as the source ) . In this section we explain how to combine the LSTMN which applies attention for intra - relation reasoning , with the encoder - decoder network whose attention module learns the inter - alignment between two sequences . 2.6.3.1 Additive and Subtractive Methods . 2.6.3.2 Multiplicative Method . 2.6.3.3 Partitive Method . To improve the parts-based representation of data some sparseness constraints have been proposed . In this paper the efficiency and execution time of five different PG algorithms and the basic multiplicative algorithm for NMF are compared . The factorization is done for an existing and proposed sparse NMF and the results are compared for all these PG methods . DNC uses LSTM as controller network that manipulates the memory matrix . In this paper , we introduce a change to DNC architecture by replacing LSTM network with multiplicative LSTM and measure the performance of the improved model by training it on three different tasks ; namely question answering task using bAbI dataset , character level modelling using harry potter text and planning search using air cargo problem . We compared the performance of the previous model to determine its behavior . In order to allow their recovery at the receiver , these pilots have to follow particular laws which permit their blind detection and avoid sending side information . In this work , we propose and investigate a multiplicative law operating in discrete frequency domain . The operation in discrete domain aims at reducing degradation due to detection and estimation error in continuous domain . In this way , we avoid the use of reserved tones and we improve the spectral efficiency . Second , in order to allow their recovery at the receiver , we investigate a multiplicative law to be applied on the transmitted pilots . This law will permit a blind detection at the receiver and avoid sending side information . We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short-term memory ( LSTM ) and multiplicative recurrent neural network architectures . mLSTM is characterised by its ability to have different recurrent transition functions for each possible input , which we argue makes it more expressive for autoregressive density estimation . Therefore , this paper aims to further study the classification accuracy of the SFIM-fused imagery . Three other simple fusion algorithms , High-Pass Filter ( HPF ) , Multiplication ( MLT ) , and Modified Brovey ( MB ) , have been employed for further evaluation of the SFIM . The study is based on a Landsat-7 ETM+ sub-scene covering the urban fringe of southeastern Fuzhou City of China . In this paper ( abstract ) , we show how weak forward security can be efficiently obtained with mediated RSA . We consider several methods , based on both multiplicative and additive mRSA and discuss their respective merits . When computing complicated functions , our protocol has more advantage in communication complexity than the direct sum method which actually executes a MPC protocol for each function . The paper also provides an efficient and generic construction to obtain from any LMSSS a multiplicative LMSSS for the same multi-access structure . This study compares the sequential and parallel efficiency of training Recurrent Neural Networks ( RNNs ) with Hessian-free optimization versus a gradient descent variant . Experiments are performed using the long short term memory ( LSTM ) architecture and the newly proposed multiplicative LSTM ( mLSTM ) architecture . Results demonstrate a number of insights into these architectures and optimization algorithms , including that Hessian-free optimization has the potential for large efficiency gains in a highly parallel setup . The k-subset D of the group G of order u is called a ( u , k , A , n)-difference set if every nonidentity element of G has exactly 1 ” representations as a difference of two elements of D ; the parameter n is defined to be equal to k I .. Using multiplicative notation for the group operation , we have that D is a difference set precisely when it satisfies in the group ring Z[G ] the equation DD'~-" = n l,+%G , ( 1.1 ) document : Multiplicative LSTM for sequence modelling We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short - term memory ( LSTM ) and multiplicative recurrent neural network architectures . When applying this to LSTM , this architecture achieves impressive sequence modelling results . The main difference between multiplicative integration LSTM and mLSTM is that mLSTM applies the Hadamard product between the multiplication of two matrices . In the case of LSTM , this allows for the potential for greater expressiveness , without significantly increasing the size of the model . With a focus on text-to-speech synthesis , we describe a set of general techniques for reducing sampling time while maintaining high output quality . We first describe a single-layer recurrent neural network , the WaveRNN , with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model . The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU . Compared to the pipeline-based front-end , our unified front-end can achieve comparable performance in polyphone disambiguation and prosody word prediction , and improve intonation phrase prediction by 0.0738 in F1 score . We also implemented the unified front-end with Tacotron and WaveRNN to build a Mandarin TTS system . The synthesized speech by that got a comparable MOS ( 4.38 ) with the pipeline-based front-end ( 4.37 ) and close to human recordings ( 4.49 ) . Then , the recovered ALAS are processed by a data-driven LAS refinement module which consists of multiple trainable convolutional layers to get the final LAS . Experimental results show that the HiNet vocoder using KDD-ASP can achieve higher quality of synthetic speech than that using conventional ASP and the WaveRNN vocoder on a text-to-speech ( TTS ) task . The proposed framework uses two encoders such that the F0 trajectory and speech waveform are both input to the system , therefore two separate codebooks are learned . We used a WaveRNN vocoder as the decoder component of VQ-VAE . Our speaker-independent VQ-VAE was trained with raw speech waveforms from multi-speaker Japanese speech databases . We find the appropriate choice of conditioning features and architectures improves both the quantitative accuracy of audio resynthesis and qualitative responsiveness to creative manipulation of conditioning . While large autoregressive models generate audio much slower than real-time , we achieve these results with a more efficient WaveRNN model , opening the door for exploring real-time interactive audio synthesis with neural networks . Furthermore , DurIAN can be used to generate high quality facial expression which can be synchronized with generated speech with/without parallel speech and face data . To improve the efficiency of speech generation , we also propose a multi-band parallel generation strategy on top of the WaveRNN model . The proposed Multi-band WaveRNN effectively reduces the total computational complexity from 9.8 to 5.5 GFLOPS , and is able to generate audio that is 6 times faster than real time on a single CPU core . We show that DurIAN could generate highly natural speech that is on par with current state of the art end-to-end systems , while at the same time avoid word skipping/repeating errors in those systems . At generation stage , the enhanced amplitude spectra are obtained by an ensemble decoding strategy , and are further combined with the phase spectra of low-bit waveforms to produce the final waveforms by inverse STFT . In our experiments on WaveRNN vocoders , an 8-bit WaveRNN with spectral enhancement outperforms a 16-bit counterpart with the same model complexity in terms of the quality of reconstructed waveforms . Besides , the proposed spectral enhancement method can also help an 8-bit WaveRNN with reduced model complexity to achieve similar subjective performance with a conventional 16-bit WaveRNN . This paper investigates real-time high-fidelity neural text-tospeech ( TTS ) systems . For real-time neural vocoders , WaveGlow is introduced and single Gaussian ( SG ) WaveRNN is proposed . The proposed SG-WaveRNN can predict continuous valued speech waveforms with half the synthesis time compared with vanilla WaveRNN with dual-softmax for 16 bit audio prediction . Additionally , a sequence-to-sequence ( seq2seq ) acoustic model ( AM ) for pitch accent languages , such as Japanese , is investigated by introducing Tacotron 2 architecture . In this paper , we propose the FeatherWave , yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding . The LPCNet , a recently proposed neural vocoder which utilized the linear predictive characteristic of speech signal in the WaveRNN architecture , can generate high quality speech with a speed faster than real-time on a single CPU core . However , LPCNet is still not efficient enough for online speech generation tasks . These new models often require powerful GPUs to achieve real-time operation , so being able to reduce their complexity would open the way for many new applications . We propose LPCNet , a WaveRNN variant that combines linear prediction with recurrent neural networks to significantly improve the efficiency of speech synthesis . We demonstrate that LPCNet can achieve significantly higher quality than WaveRNN for the same network size and that high quality LPCNet speech synthesis is achievable with a complexity under 3 GFLOPS . Following [ 3 ] , in this work , we demonstrate similar scalability and transfer for Recurrent Neural Networks ( RNNs ) for Natural Language tasks . By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs , we are able to train a character-level 4096-dimension multiplicative LSTM ( mLSTM ) [ 4 ] for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset [ 5 ] in four hours . This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset [ 3 ] . We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level language modelling tasks . In this version of the paper , we regularise mLSTM to achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize . We also apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a character level entropy of 1.26 bits/char , corresponding to a word level perplexity of 88.8 , which is comparable to word level LSTMs regularised in similar ways on the same task . If we can decompose a sentence into sub-events related to it , and judge that the sub-events in the hypothetical text are contained in the sub-events of the premise text , then we can determine the implication between the two sentences . The mLSTM model achieved an accuracy of 86.1 % on the SNLI corpus , which is the best level at present , but it is not effective on other small corpus . This article aims to improve the mLSTM model , establishing the mGRU model based on GRU ( Gated Recurrent Unit ) , and verify the effect of the model on SNLI corpus and multiNLI corpora . mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework . Based on mWDN , we further propose two deep learning models called Residual Classification Flow ( RCF ) and multi-frequecy Long Short-Term Memory ( mLSTM ) for time series classification and forecasting , respectively . The two models take all or partial mWDN decomposed sub-series in different frequencies as input , and resort to the back propagation algorithm to learn all the parameters globally , which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks . Descriptions in the FlickrNYC dataset vary dramatically ranging from short term-descriptions to long paragraph-descriptions and can describe any visual aspects , or even refer to objects that are not depicted . To deal with the imbalanced and noisy situation and to fully explore the dataset itself , we propose a novel guiding textual feature extracted utilizing a multimodal LSTM ( mLSTM ) model . Training of mLSTM is based on the portion of data in which the image content and the corresponding descriptions are strongly bonded . document : Multiplicative LSTM for sequence modelling We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short - term memory ( LSTM ) and multiplicative recurrent neural network architectures . mLSTM is characterised by its ability to have different recurrent transition functions for each possible input , which we argue makes it more expressive for autoregressive density estimation . We test our best model from these experiments on the WikiText - 2 dataset to measure performance on smaller scale character level language modelling , and to compare with word level models . Previous versions of the paper also report a character level result on Penn Treebank dataset of 1.35 bits / char with an unregularised mLSTM , however we do not include this experiment in this version as we have no results with our updated training and regularisation methodology . subsection : Hutter Prize dataset We firstly consider a standard unregularised mLSTM trained with this methodology . We then experiment with an mLSTM with a linear embedding layer and weight normalization on recurrent weights ( mLSTM + emb + WN ) , which is similar to the mLSTM architecture used in , which was built off our initial work . We also consider regularisation of the later model with variational dropout ( mLSTM + emb + The standard unregularised LSTM used 1900 hidden units and 20 million parameters . The weight normalized mLSTM used 1900 hidden units , and a linear embedding layer of 400 , giving it 22 million parameters . The large embedding layer was used because it was found to work well with dropout . mLSTM showed an improvement over our baseline and the previously reported best deep LSTM variant . We also ran experiments to compare a large mLSTM with other reported experiments . We trained an mLSTM with hidden dimensionality of 1900 on the text8 dataset . This is because the entropy of the file after tokenization ( which word level models measure ) will always be less than or equal to the entropy of the file before tokenization ( which byte level models measure ) . We trained the mLSTM configuration from the Hutter Prize dataset , using an embedding layer , weight normalization , and a variational dropout of 0.5 in both the hidden and embedding layer , to model WikiText - 2 at the byte level . This model contained 46 million parameters , which is larger than most word level models that use tied input and output embeddings to share parameters , but similar in size to untied word level models on this dataset . Byte mLSTM achieves a byte - level test set cross entropy of bits / char , corresponding to a perplexity of . Despite all the disadvantages faced by character level models , byte level mLSTM achieves similar word level perplexity to previous word - level LSTM baselines that also use variational dropout for regularisation . Byte mLSTM does not perform as well as word - level models that use adaptive add - on methods or very recent advances in regularisation / hyper - parameter tuning , however it could likely benefit from these advances as well . Recent work has shown that language models – both RNN variants like the multiplicative LSTM ( mLSTM ) , as well as the attention - based Transformer network – can be trained efficiently over very large datasets , and that the resulting models can be transferred to downstream language understanding problems , often matching or exceeding the previous state of the art approaches on academic datasets . However , how well do these models perform on practical text classification problems , with real world data ? aligns each sentence word - by - word with attention on top of LSTMs . enforces cross sentence attention word - by - word matching with the proprosed mLSTM model . proposes long short - term memory - network ( LSTMN ) with deep attention fusion that links the current word to previous word stored in memory . To help alleviate this , a number of computational tools have been developed which integrate gene expression data with molecular interaction networks in order to predict these factors . One such approach is Mogrify , an algorithm which ranks transcriptions factors based on their regulatory influence in different cell types and tissues . These ranks are then used to identify a nonredundant set of transcription factors to promote cell conversion between any two cell types/tissues . These stains label important neuroanatomical landmarks that are used for stacking the serial sections during reconstruction . In addition , we show that the use of two software applications , ir-Tweak and Mogrifier , in conjunction with a volume flattening procedure enable scientists to adeptly work with digitized volumes despite tears and thickness variations within tissue sections . These applications make processing large-scale brain mosaics more efficient .
Attention based end-to-end speech synthesis achieves better performance in both prosody and quality compared to the conventional " front-end"-"back-end " structure . But training such end-to-end framework is usually time-consuming because of the use of recurrent neural networks . To enable parallel calculation and long-range dependency modeling , a solely self-attention based framework named Transformer is proposed recently in the end-to-end family .
The paper provides an overview of modern recurrent neural network architectures that can be used as a model ( building tool ) for diagnosis and predicting of infocommunication systems elements . The paper presents arguments in favor of the using of recurrent neural networks .
Insufficient level of geographic information in nucleotide sequence repositories such as GenBank motivates the use of natural language processing methods for extracting geographic location names ( toponyms ) in the scientific article associated with the sequence , and disambiguating the locations to their co-ordinates . In this paper , we present an extensive study of multiple recurrent neural network architectures for the task of extracting geographic locations and their effective contribution to the disambiguation task using population heuristics . The methods presented in this paper achieve a strict detection F1 score of 0.94 , disambiguation accuracy of 91 % and an overall resolution F1 score of 0.88 that are significantly higher than previously developed methods , improving our capability to find the location of infected hosts and enrich metadata information .
DCR is able to predict answers of variable lengths , whereas previous neural RC models primarily focused on predicting single tokens or entities . DCR encodes a document and an input question with recurrent neural networks , and then applies a word - by - word attention mechanism to acquire question - aware representations for the document , followed by the generation of chunk representations and a ranking module to propose the top - ranked chunk as the answer . Experimental results show that DCR achieves state - of - the - art exact match and F1 scores on the SQuAD dataset .
Natural language processing tasks such as machine translation and textual entailment are concerned with modeling two sequences rather than a single one . A standard tool for modeling two sequences with recurrent networks is the encoder - decoder architecture where the second sequence ( also known as the target ) is being processed conditioned on the first one ( also known as the source ) . In this section we explain how to combine the LSTMN which applies attention for intra - relation reasoning , with the encoder - decoder network whose attention module learns the inter - alignment between two sequences .
2.6.3.1 Additive and Subtractive Methods . 2.6.3.2 Multiplicative Method . 2.6.3.3 Partitive Method .
To improve the parts-based representation of data some sparseness constraints have been proposed . In this paper the efficiency and execution time of five different PG algorithms and the basic multiplicative algorithm for NMF are compared . The factorization is done for an existing and proposed sparse NMF and the results are compared for all these PG methods .
DNC uses LSTM as controller network that manipulates the memory matrix . In this paper , we introduce a change to DNC architecture by replacing LSTM network with multiplicative LSTM and measure the performance of the improved model by training it on three different tasks ; namely question answering task using bAbI dataset , character level modelling using harry potter text and planning search using air cargo problem . We compared the performance of the previous model to determine its behavior .
In order to allow their recovery at the receiver , these pilots have to follow particular laws which permit their blind detection and avoid sending side information . In this work , we propose and investigate a multiplicative law operating in discrete frequency domain . The operation in discrete domain aims at reducing degradation due to detection and estimation error in continuous domain .
In this way , we avoid the use of reserved tones and we improve the spectral efficiency . Second , in order to allow their recovery at the receiver , we investigate a multiplicative law to be applied on the transmitted pilots . This law will permit a blind detection at the receiver and avoid sending side information .
We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short-term memory ( LSTM ) and multiplicative recurrent neural network architectures . mLSTM is characterised by its ability to have different recurrent transition functions for each possible input , which we argue makes it more expressive for autoregressive density estimation .
Therefore , this paper aims to further study the classification accuracy of the SFIM-fused imagery . Three other simple fusion algorithms , High-Pass Filter ( HPF ) , Multiplication ( MLT ) , and Modified Brovey ( MB ) , have been employed for further evaluation of the SFIM . The study is based on a Landsat-7 ETM+ sub-scene covering the urban fringe of southeastern Fuzhou City of China .
In this paper ( abstract ) , we show how weak forward security can be efficiently obtained with mediated RSA . We consider several methods , based on both multiplicative and additive mRSA and discuss their respective merits .
When computing complicated functions , our protocol has more advantage in communication complexity than the direct sum method which actually executes a MPC protocol for each function . The paper also provides an efficient and generic construction to obtain from any LMSSS a multiplicative LMSSS for the same multi-access structure .
This study compares the sequential and parallel efficiency of training Recurrent Neural Networks ( RNNs ) with Hessian-free optimization versus a gradient descent variant . Experiments are performed using the long short term memory ( LSTM ) architecture and the newly proposed multiplicative LSTM ( mLSTM ) architecture . Results demonstrate a number of insights into these architectures and optimization algorithms , including that Hessian-free optimization has the potential for large efficiency gains in a highly parallel setup .
The k-subset D of the group G of order u is called a ( u , k , A , n)-difference set if every nonidentity element of G has exactly 1 ” representations as a difference of two elements of D ; the parameter n is defined to be equal to k I .. Using multiplicative notation for the group operation , we have that D is a difference set precisely when it satisfies in the group ring Z[G ] the equation DD'~-" = n l,+%G , ( 1.1 )
document : Multiplicative LSTM for sequence modelling We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short - term memory ( LSTM ) and multiplicative recurrent neural network architectures .
When applying this to LSTM , this architecture achieves impressive sequence modelling results . The main difference between multiplicative integration LSTM and mLSTM is that mLSTM applies the Hadamard product between the multiplication of two matrices . In the case of LSTM , this allows for the potential for greater expressiveness , without significantly increasing the size of the model .
With a focus on text-to-speech synthesis , we describe a set of general techniques for reducing sampling time while maintaining high output quality . We first describe a single-layer recurrent neural network , the WaveRNN , with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model . The compact form of the network makes it possible to generate 24kHz 16-bit audio 4x faster than real time on a GPU .
Compared to the pipeline-based front-end , our unified front-end can achieve comparable performance in polyphone disambiguation and prosody word prediction , and improve intonation phrase prediction by 0.0738 in F1 score . We also implemented the unified front-end with Tacotron and WaveRNN to build a Mandarin TTS system . The synthesized speech by that got a comparable MOS ( 4.38 ) with the pipeline-based front-end ( 4.37 ) and close to human recordings ( 4.49 ) .
Then , the recovered ALAS are processed by a data-driven LAS refinement module which consists of multiple trainable convolutional layers to get the final LAS . Experimental results show that the HiNet vocoder using KDD-ASP can achieve higher quality of synthetic speech than that using conventional ASP and the WaveRNN vocoder on a text-to-speech ( TTS ) task .
The proposed framework uses two encoders such that the F0 trajectory and speech waveform are both input to the system , therefore two separate codebooks are learned . We used a WaveRNN vocoder as the decoder component of VQ-VAE . Our speaker-independent VQ-VAE was trained with raw speech waveforms from multi-speaker Japanese speech databases .
We find the appropriate choice of conditioning features and architectures improves both the quantitative accuracy of audio resynthesis and qualitative responsiveness to creative manipulation of conditioning . While large autoregressive models generate audio much slower than real-time , we achieve these results with a more efficient WaveRNN model , opening the door for exploring real-time interactive audio synthesis with neural networks .
Furthermore , DurIAN can be used to generate high quality facial expression which can be synchronized with generated speech with/without parallel speech and face data . To improve the efficiency of speech generation , we also propose a multi-band parallel generation strategy on top of the WaveRNN model . The proposed Multi-band WaveRNN effectively reduces the total computational complexity from 9.8 to 5.5 GFLOPS , and is able to generate audio that is 6 times faster than real time on a single CPU core . We show that DurIAN could generate highly natural speech that is on par with current state of the art end-to-end systems , while at the same time avoid word skipping/repeating errors in those systems .
At generation stage , the enhanced amplitude spectra are obtained by an ensemble decoding strategy , and are further combined with the phase spectra of low-bit waveforms to produce the final waveforms by inverse STFT . In our experiments on WaveRNN vocoders , an 8-bit WaveRNN with spectral enhancement outperforms a 16-bit counterpart with the same model complexity in terms of the quality of reconstructed waveforms . Besides , the proposed spectral enhancement method can also help an 8-bit WaveRNN with reduced model complexity to achieve similar subjective performance with a conventional 16-bit WaveRNN .
This paper investigates real-time high-fidelity neural text-tospeech ( TTS ) systems . For real-time neural vocoders , WaveGlow is introduced and single Gaussian ( SG ) WaveRNN is proposed . The proposed SG-WaveRNN can predict continuous valued speech waveforms with half the synthesis time compared with vanilla WaveRNN with dual-softmax for 16 bit audio prediction . Additionally , a sequence-to-sequence ( seq2seq ) acoustic model ( AM ) for pitch accent languages , such as Japanese , is investigated by introducing Tacotron 2 architecture .
In this paper , we propose the FeatherWave , yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding . The LPCNet , a recently proposed neural vocoder which utilized the linear predictive characteristic of speech signal in the WaveRNN architecture , can generate high quality speech with a speed faster than real-time on a single CPU core . However , LPCNet is still not efficient enough for online speech generation tasks .
These new models often require powerful GPUs to achieve real-time operation , so being able to reduce their complexity would open the way for many new applications . We propose LPCNet , a WaveRNN variant that combines linear prediction with recurrent neural networks to significantly improve the efficiency of speech synthesis . We demonstrate that LPCNet can achieve significantly higher quality than WaveRNN for the same network size and that high quality LPCNet speech synthesis is achievable with a complexity under 3 GFLOPS .
Following [ 3 ] , in this work , we demonstrate similar scalability and transfer for Recurrent Neural Networks ( RNNs ) for Natural Language tasks . By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs , we are able to train a character-level 4096-dimension multiplicative LSTM ( mLSTM ) [ 4 ] for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset [ 5 ] in four hours . This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset [ 3 ] .
We demonstrate empirically that mLSTM outperforms standard LSTM and its deep variants for a range of character level language modelling tasks . In this version of the paper , we regularise mLSTM to achieve 1.27 bits/char on text8 and 1.24 bits/char on Hutter Prize . We also apply a purely byte-level mLSTM on the WikiText-2 dataset to achieve a character level entropy of 1.26 bits/char , corresponding to a word level perplexity of 88.8 , which is comparable to word level LSTMs regularised in similar ways on the same task .
If we can decompose a sentence into sub-events related to it , and judge that the sub-events in the hypothetical text are contained in the sub-events of the premise text , then we can determine the implication between the two sentences . The mLSTM model achieved an accuracy of 86.1 % on the SNLI corpus , which is the best level at present , but it is not effective on other small corpus . This article aims to improve the mLSTM model , establishing the mGRU model based on GRU ( Gated Recurrent Unit ) , and verify the effect of the model on SNLI corpus and multiNLI corpora .
mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework . Based on mWDN , we further propose two deep learning models called Residual Classification Flow ( RCF ) and multi-frequecy Long Short-Term Memory ( mLSTM ) for time series classification and forecasting , respectively . The two models take all or partial mWDN decomposed sub-series in different frequencies as input , and resort to the back propagation algorithm to learn all the parameters globally , which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks .
Descriptions in the FlickrNYC dataset vary dramatically ranging from short term-descriptions to long paragraph-descriptions and can describe any visual aspects , or even refer to objects that are not depicted . To deal with the imbalanced and noisy situation and to fully explore the dataset itself , we propose a novel guiding textual feature extracted utilizing a multimodal LSTM ( mLSTM ) model . Training of mLSTM is based on the portion of data in which the image content and the corresponding descriptions are strongly bonded .
document : Multiplicative LSTM for sequence modelling We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short - term memory ( LSTM ) and multiplicative recurrent neural network architectures . mLSTM is characterised by its ability to have different recurrent transition functions for each possible input , which we argue makes it more expressive for autoregressive density estimation .
We test our best model from these experiments on the WikiText - 2 dataset to measure performance on smaller scale character level language modelling , and to compare with word level models . Previous versions of the paper also report a character level result on Penn Treebank dataset of 1.35 bits / char with an unregularised mLSTM , however we do not include this experiment in this version as we have no results with our updated training and regularisation methodology . subsection : Hutter Prize dataset
We firstly consider a standard unregularised mLSTM trained with this methodology . We then experiment with an mLSTM with a linear embedding layer and weight normalization on recurrent weights ( mLSTM + emb + WN ) , which is similar to the mLSTM architecture used in , which was built off our initial work . We also consider regularisation of the later model with variational dropout ( mLSTM + emb +
The standard unregularised LSTM used 1900 hidden units and 20 million parameters . The weight normalized mLSTM used 1900 hidden units , and a linear embedding layer of 400 , giving it 22 million parameters . The large embedding layer was used because it was found to work well with dropout .
mLSTM showed an improvement over our baseline and the previously reported best deep LSTM variant . We also ran experiments to compare a large mLSTM with other reported experiments . We trained an mLSTM with hidden dimensionality of 1900 on the text8 dataset .
This is because the entropy of the file after tokenization ( which word level models measure ) will always be less than or equal to the entropy of the file before tokenization ( which byte level models measure ) . We trained the mLSTM configuration from the Hutter Prize dataset , using an embedding layer , weight normalization , and a variational dropout of 0.5 in both the hidden and embedding layer , to model WikiText - 2 at the byte level . This model contained 46 million parameters , which is larger than most word level models that use tied input and output embeddings to share parameters , but similar in size to untied word level models on this dataset .
Byte mLSTM achieves a byte - level test set cross entropy of bits / char , corresponding to a perplexity of . Despite all the disadvantages faced by character level models , byte level mLSTM achieves similar word level perplexity to previous word - level LSTM baselines that also use variational dropout for regularisation . Byte mLSTM does not perform as well as word - level models that use adaptive add - on methods or very recent advances in regularisation / hyper - parameter tuning , however it could likely benefit from these advances as well .
Recent work has shown that language models – both RNN variants like the multiplicative LSTM ( mLSTM ) , as well as the attention - based Transformer network – can be trained efficiently over very large datasets , and that the resulting models can be transferred to downstream language understanding problems , often matching or exceeding the previous state of the art approaches on academic datasets . However , how well do these models perform on practical text classification problems , with real world data ?
aligns each sentence word - by - word with attention on top of LSTMs . enforces cross sentence attention word - by - word matching with the proprosed mLSTM model . proposes long short - term memory - network ( LSTMN ) with deep attention fusion that links the current word to previous word stored in memory .
To help alleviate this , a number of computational tools have been developed which integrate gene expression data with molecular interaction networks in order to predict these factors . One such approach is Mogrify , an algorithm which ranks transcriptions factors based on their regulatory influence in different cell types and tissues . These ranks are then used to identify a nonredundant set of transcription factors to promote cell conversion between any two cell types/tissues .
These stains label important neuroanatomical landmarks that are used for stacking the serial sections during reconstruction . In addition , we show that the use of two software applications , ir-Tweak and Mogrifier , in conjunction with a volume flattening procedure enable scientists to adeptly work with digitized volumes despite tears and thickness variations within tissue sections . These applications make processing large-scale brain mosaics more efficient .
On the localness modeling for the self-attention based end-to-end speech synthesis Using Modern Architectures of Recurrent Neural Networks for Technical Diagnosis of Complex Systems Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension Long Short-Term Memory-Networks for Machine Reading Derivative spectrophotometry : low and higher order Nonnegative matrix factorization using projected gradient algorithms with sparseness constraints An Improved Differential Neural Computer Model Using Multiplicative LSTM PAPR Reduction Approach Based on Channel Estimation Pilots for Next Generations Broadcasting Systems On the performance of joint channel estimation and PAPR reduction scheme Multiplicative LSTM for sequence modelling Assessment of the SFIM algorithm Weak Forward Security in Mediated RSA Parallel Multi-party Computation from Linear Multi-secret Sharing Schemes On the Efficiency of Recurrent Neural Network Optimization Algorithms Variations on a Scheme of McFarland for Noncyclic Difference Sets Multiplicative LSTM for sequence modelling Efficient Neural Audio Synthesis A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis Knowledge-and-Data-Driven Amplitude Spectrum Prediction for Hierarchical Neural Vocoders Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction Fast and Flexible Neural Audio Synthesis DurIAN: Duration Informed Attention Network For Multimodal Synthesis Dnn-based Spectral Enhancement for Neural Waveform Generators with Low-bit Quantization Real-Time Neural Text-to-Speech with Sequence-to-Sequence Acoustic Model and WaveGlow or Single Gaussian WaveRNN Vocoders FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction LPCNET: Improving Neural Speech Synthesis through Linear Prediction Large Scale Language Modeling: Converging on 40GB of Text in Four Hours Multiplicative LSTM for sequence modelling Text Implication Recognition Learning with Gated Recurrent Unit Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis Self-Guiding Multimodal LSTM—When We Do Not Have a Perfect Training Dataset for Image Captioning Multiplicative LSTM for sequence modelling Practical Text Classification With Large Pre-Trained Language Models Natural Language Inference over Interaction Space Molecular Interaction Networks to Select Factors for Cell Conversion. Three-Dimensional Reconstruction of Serial Mouse Brain Sections: Solution for Flattening High-Resolution Large-Scale Mosaics
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PSB EMNLP ICNC SCN ICLR ICASSP ISMIR ICASSP INTERSPEECH SBAC-PAD CSAI/ICIMT KDD ICLR ArXiv ICLR
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