Exploring efficient neural architectures for linguistic-acoustic mapping in text-to-speech

Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance–speed trade-off. The first mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.Peer ReviewedPostprint (published version

Spanish statistical parametric speech synthesis using a neural vocoder

During the 2000s decade, unit-selection based text-to-speech was the dominant commercial technology. Meanwhile, the TTS research community has made a big effort to push statistical-parametric speech synthesis to get similar quality and more flexibility on the synthetically generated voice. During last years, deep learning advances applied to speech synthesis have filled the gap, specially when neural vocoders substitute traditional signal-processing based vocoders. In this paper we propose to substitute the waveform generation vocoder of MUSA, our Spanish TTS, with SampleRNN, a neural vocoder which was recently proposed as a deep autoregressive raw waveform generation model. MUSA uses recurrent neural networks to predict vocoder parameters (MFCC and logF0) from linguistic features. Then, the Ahocoder vocoder is used to recover the speech waveform out of the predicted parameters. In the first system SampleRNN is extended to generate speech conditioned on the Ahocoder generated parameters (mfcc and logF0), where two configurations have been considered to train the system. First, the parameters derived from the signal using Ahocoder are used. Secondly, the system is trained with the parameters predicted by MUSA, where SampleRNN and MUSA are jointly optimized. The subjective evaluation shows that the second system outperforms both the original Ahocoder and SampleRNN as an independent neural vocoder.Peer ReviewedPostprint (published version

Efficient, end-to-end and self-supervised methods for speech processing and generation

Deep learning has affected the speech processing and generation fields in many directions. First, end-to-end architectures allow the direct injection and synthesis of waveform samples. Secondly, the exploration of efficient solutions allow to implement these systems in computationally restricted environments, like smartphones. Finally, the latest trends exploit audio-visual data with least supervision. In this thesis these three directions are explored. Firstly, we propose the use of recent pseudo-recurrent structures, like self-attention models and quasi-recurrent networks, to build acoustic models for text-to-speech. The proposed system, QLAD, turns out to synthesize faster on CPU and GPU than its recurrent counterpart whilst preserving the good synthesis quality level, which is competitive with state of the art vocoder-based models. Then, a generative adversarial network is proposed for speech enhancement, named SEGAN. This model works as a speech-to-speech conversion system in time-domain, where a single inference operation is needed for all samples to operate through a fully convolutional structure. This implies an increment in modeling efficiency with respect to other existing models, which are auto-regressive and also work in time-domain. SEGAN achieves prominent results in noise supression and preservation of speech naturalness and intelligibility when compared to the other classic and deep regression based systems. We also show that SEGAN is efficient in transferring its operations to new languages and noises. A SEGAN trained for English performs similarly to this language on Catalan and Korean with only 24 seconds of adaptation data. Finally, we unveil the generative capacity of the model to recover signals from several distortions. We hence propose the concept of generalized speech enhancement. First, the model proofs to be effective to recover voiced speech from whispered one. Then the model is scaled up to solve other distortions that require a recomposition of damaged parts of the signal, like extending the bandwidth or recovering lost temporal sections, among others. The model improves by including additional acoustic losses in a multi-task setup to impose a relevant perceptual weighting on the generated result. Moreover, a two-step training schedule is also proposed to stabilize the adversarial training after the addition of such losses, and both components boost SEGAN's performance across distortions.Finally, we propose a problem-agnostic speech encoder, named PASE, together with the framework to train it. PASE is a fully convolutional network that yields compact representations from speech waveforms. These representations contain abstract information like the speaker identity, the prosodic features or the spoken contents. A self-supervised framework is also proposed to train this encoder, which suposes a new step towards unsupervised learning for speech processing. Once the encoder is trained, it can be exported to solve different tasks that require speech as input. We first explore the performance of PASE codes to solve speaker recognition, emotion recognition and speech recognition. PASE works competitively well compared to well-designed classic features in these tasks, specially after some supervised adaptation. Finally, PASE also provides good descriptors of identity for multi-speaker modeling in text-to-speech, which is advantageous to model novel identities without retraining the model.L'aprenentatge profund ha afectat els camps de processament i generació de la parla en vàries direccions. Primer, les arquitectures fi-a-fi permeten la injecció i síntesi de mostres temporals directament. D'altra banda, amb l'exploració de solucions eficients permet l'aplicació d'aquests sistemes en entorns de computació restringida, com els telèfons intel·ligents. Finalment, les darreres tendències exploren les dades d'àudio i veu per derivar-ne representacions amb la mínima supervisió. En aquesta tesi precisament s'exploren aquestes tres direccions. Primer de tot, es proposa l'ús d'estructures pseudo-recurrents recents, com els models d’auto atenció i les xarxes quasi-recurrents, per a construir models acústics text-a-veu. Així, el sistema QLAD proposat en aquest treball sintetitza més ràpid en CPU i GPU que el seu homòleg recurrent, preservant el mateix nivell de qualitat de síntesi, competitiu amb l'estat de l'art en models basats en vocoder. A continuació es proposa un model de xarxa adversària generativa per a millora de veu, anomenat SEGAN. Aquest model fa conversions de veu-a-veu en temps amb una sola operació d'inferència sobre una estructura purament convolucional. Això implica un increment en l'eficiència respecte altres models existents auto regressius i que també treballen en el domini temporal. La SEGAN aconsegueix resultats prominents d'extracció de soroll i preservació de la naturalitat i la intel·ligibilitat de la veu comparat amb altres sistemes clàssics i models regressius basats en xarxes neuronals profundes en espectre. També es demostra que la SEGAN és eficient transferint les seves operacions a nous llenguatges i sorolls. Així, un model SEGAN entrenat en Anglès aconsegueix un rendiment comparable a aquesta llengua quan el transferim al català o al coreà amb només 24 segons de dades d'adaptació. Finalment, explorem l'ús de tota la capacitat generativa del model i l’apliquem a recuperació de senyals de veu malmeses per vàries distorsions severes. Això ho anomenem millora de la parla generalitzada. Primer, el model demostra ser efectiu per a la tasca de recuperació de senyal sonoritzat a partir de senyal xiuxiuejat. Posteriorment, el model escala a poder resoldre altres distorsions que requereixen una reconstrucció de parts del senyal que s’han malmès, com extensió d’ample de banda i recuperació de seccions temporals perdudes, entre d’altres. En aquesta última aplicació del model, el fet d’incloure funcions de pèrdua acústicament rellevants incrementa la naturalitat del resultat final, en una estructura multi-tasca que prediu característiques acústiques a la sortida de la xarxa discriminadora de la nostra GAN. També es proposa fer un entrenament en dues etapes del sistema SEGAN, el qual mostra un increment significatiu de l’equilibri en la sinèrgia adversària i la qualitat generada finalment després d’afegir les funcions acústiques. Finalment, proposem un codificador de veu agnòstic al problema, anomenat PASE, juntament amb el conjunt d’eines per entrenar-lo. El PASE és un sistema purament convolucional que crea representacions compactes de trames de veu. Aquestes representacions contenen informació abstracta com identitat del parlant, les característiques prosòdiques i els continguts lingüístics. També es proposa un entorn auto-supervisat multi-tasca per tal d’entrenar aquest sistema, el qual suposa un avenç en el terreny de l’aprenentatge no supervisat en l’àmbit del processament de la parla. Una vegada el codificador esta entrenat, es pot exportar per a solventar diferents tasques que requereixin tenir senyals de veu a l’entrada. Primer explorem el rendiment d’aquest codificador per a solventar tasques de reconeixement del parlant, de l’emoció i de la parla, mostrant-se efectiu especialment si s’ajusta la representació de manera supervisada amb un conjunt de dades d’adaptació

Efficient, end-to-end and self-supervised methods for speech processing and generation

Deep learning has affected the speech processing and generation fields in many directions. First, end-to-end architectures allow the direct injection and synthesis of waveform samples. Secondly, the exploration of efficient solutions allow to implement these systems in computationally restricted environments, like smartphones. Finally, the latest trends exploit audio-visual data with least supervision. In this thesis these three directions are explored. Firstly, we propose the use of recent pseudo-recurrent structures, like self-attention models and quasi-recurrent networks, to build acoustic models for text-to-speech. The proposed system, QLAD, turns out to synthesize faster on CPU and GPU than its recurrent counterpart whilst preserving the good synthesis quality level, which is competitive with state of the art vocoder-based models. Then, a generative adversarial network is proposed for speech enhancement, named SEGAN. This model works as a speech-to-speech conversion system in time-domain, where a single inference operation is needed for all samples to operate through a fully convolutional structure. This implies an increment in modeling efficiency with respect to other existing models, which are auto-regressive and also work in time-domain. SEGAN achieves prominent results in noise supression and preservation of speech naturalness and intelligibility when compared to the other classic and deep regression based systems. We also show that SEGAN is efficient in transferring its operations to new languages and noises. A SEGAN trained for English performs similarly to this language on Catalan and Korean with only 24 seconds of adaptation data. Finally, we unveil the generative capacity of the model to recover signals from several distortions. We hence propose the concept of generalized speech enhancement. First, the model proofs to be effective to recover voiced speech from whispered one. Then the model is scaled up to solve other distortions that require a recomposition of damaged parts of the signal, like extending the bandwidth or recovering lost temporal sections, among others. The model improves by including additional acoustic losses in a multi-task setup to impose a relevant perceptual weighting on the generated result. Moreover, a two-step training schedule is also proposed to stabilize the adversarial training after the addition of such losses, and both components boost SEGAN's performance across distortions.Finally, we propose a problem-agnostic speech encoder, named PASE, together with the framework to train it. PASE is a fully convolutional network that yields compact representations from speech waveforms. These representations contain abstract information like the speaker identity, the prosodic features or the spoken contents. A self-supervised framework is also proposed to train this encoder, which suposes a new step towards unsupervised learning for speech processing. Once the encoder is trained, it can be exported to solve different tasks that require speech as input. We first explore the performance of PASE codes to solve speaker recognition, emotion recognition and speech recognition. PASE works competitively well compared to well-designed classic features in these tasks, specially after some supervised adaptation. Finally, PASE also provides good descriptors of identity for multi-speaker modeling in text-to-speech, which is advantageous to model novel identities without retraining the model.L'aprenentatge profund ha afectat els camps de processament i generació de la parla en vàries direccions. Primer, les arquitectures fi-a-fi permeten la injecció i síntesi de mostres temporals directament. D'altra banda, amb l'exploració de solucions eficients permet l'aplicació d'aquests sistemes en entorns de computació restringida, com els telèfons intel·ligents. Finalment, les darreres tendències exploren les dades d'àudio i veu per derivar-ne representacions amb la mínima supervisió. En aquesta tesi precisament s'exploren aquestes tres direccions. Primer de tot, es proposa l'ús d'estructures pseudo-recurrents recents, com els models d’auto atenció i les xarxes quasi-recurrents, per a construir models acústics text-a-veu. Així, el sistema QLAD proposat en aquest treball sintetitza més ràpid en CPU i GPU que el seu homòleg recurrent, preservant el mateix nivell de qualitat de síntesi, competitiu amb l'estat de l'art en models basats en vocoder. A continuació es proposa un model de xarxa adversària generativa per a millora de veu, anomenat SEGAN. Aquest model fa conversions de veu-a-veu en temps amb una sola operació d'inferència sobre una estructura purament convolucional. Això implica un increment en l'eficiència respecte altres models existents auto regressius i que també treballen en el domini temporal. La SEGAN aconsegueix resultats prominents d'extracció de soroll i preservació de la naturalitat i la intel·ligibilitat de la veu comparat amb altres sistemes clàssics i models regressius basats en xarxes neuronals profundes en espectre. També es demostra que la SEGAN és eficient transferint les seves operacions a nous llenguatges i sorolls. Així, un model SEGAN entrenat en Anglès aconsegueix un rendiment comparable a aquesta llengua quan el transferim al català o al coreà amb només 24 segons de dades d'adaptació. Finalment, explorem l'ús de tota la capacitat generativa del model i l’apliquem a recuperació de senyals de veu malmeses per vàries distorsions severes. Això ho anomenem millora de la parla generalitzada. Primer, el model demostra ser efectiu per a la tasca de recuperació de senyal sonoritzat a partir de senyal xiuxiuejat. Posteriorment, el model escala a poder resoldre altres distorsions que requereixen una reconstrucció de parts del senyal que s’han malmès, com extensió d’ample de banda i recuperació de seccions temporals perdudes, entre d’altres. En aquesta última aplicació del model, el fet d’incloure funcions de pèrdua acústicament rellevants incrementa la naturalitat del resultat final, en una estructura multi-tasca que prediu característiques acústiques a la sortida de la xarxa discriminadora de la nostra GAN. També es proposa fer un entrenament en dues etapes del sistema SEGAN, el qual mostra un increment significatiu de l’equilibri en la sinèrgia adversària i la qualitat generada finalment després d’afegir les funcions acústiques. Finalment, proposem un codificador de veu agnòstic al problema, anomenat PASE, juntament amb el conjunt d’eines per entrenar-lo. El PASE és un sistema purament convolucional que crea representacions compactes de trames de veu. Aquestes representacions contenen informació abstracta com identitat del parlant, les característiques prosòdiques i els continguts lingüístics. També es proposa un entorn auto-supervisat multi-tasca per tal d’entrenar aquest sistema, el qual suposa un avenç en el terreny de l’aprenentatge no supervisat en l’àmbit del processament de la parla. Una vegada el codificador esta entrenat, es pot exportar per a solventar diferents tasques que requereixin tenir senyals de veu a l’entrada. Primer explorem el rendiment d’aquest codificador per a solventar tasques de reconeixement del parlant, de l’emoció i de la parla, mostrant-se efectiu especialment si s’ajusta la representació de manera supervisada amb un conjunt de dades d’adaptació.Postprint (published version

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

자동 운율 복제를 위한 모음 길이와 기본 주파수 예측

학위논문 (석사)-- 서울대학교 대학원 : 인문대학 협동과정 인지과학전공, 2018. 8. 정민화.The use of computers to help people improve their pronunciation skills of a foreign language has rapidly increased in the last decades. Majority of such Computer-Assisted Pronunciation Training (CAPT) systems have been focused on teaching correct pronunciation of segments only, however, while prosody received much less attention. One of the new approaches to prosody training is self-imitation learning. Prosodic features from a native utterance are transplanted onto learners own speech, and given back as corrective feedback. The main drawback is that this technique requires two identical sets of native and non-native utterances, which makes its actual implementation cumbersome and inflexible. As a preliminary research towards developing a new method of prosody transplantation, the first part of the study surveys previous related works and points out their advantages and drawbacks. We also compare prosodic systems of Korean and English, point out major areas of mistakes that Korean learners of English tend to do, and then we analyze acoustic features that this mistakes are correlated with. We suggest that transplantation of vowel duration and fundamental frequency will be the most effective for self-imitation learning by Korean speakers of English. The second part of this study introduces a new proposed model for prosody transplantation. Instead of transplanting acoustic values from a pre-recorded utterance, we suggest to use a deep neural network (DNN) based system to predict them instead. Three different models are built and described: baseline recurrent neural network (RNN), long short-term memory (LSTM) model and gated recurrent unit (GRU) model. The models were trained on Boston University Radio Speech Corpus, using a minimal set of relevant input features. The models were compared with each other, as well as with state-of-the-art prosody prediction systems from speech synthesis research. Implementation of the proposed prediction model in automatic prosody transplantation is described and the results are analyzed. A perceptual evaluation by native speakers was carried out. Accentedness and comprehensibility ratings of modified and original non-native utterances were compared with each other. The results showed that duration transplantation can lead to the improvements in comprehensibility score. This study lays the groundwork for a fully automatic self-imitation prosody training system and its results can be used to help Korean learners master problematic areas of English prosody, such as sentence stress.Chapter 1. Introduction . 10 1.1 Background. 10 1.2 Research Objective 12 1.3 Research Outline. 15 Chapter 2. Related Works. 16 2.1 Self-imitation Prosody Training. 16 2.1.1 Prosody Transplantation Methods . 18 2.1.2 Effects of Prosody Transplantation on Accentedness Rating 23 2.1.3 Effects of Self-Imitation Learning on Proficiency Rating 26 2.2 Prosody of Korean-accented English Speech 28 2.2.1 Prosodic Systems of Korean and English 28 2.2.2 Common Prosodic Mistakes. 29 2.3 Deep Learning Based Prosody Prediction 34 2.3.1 Deep Learning . 34 2.3.2 Recurrent Neural Networks 35 2.3.2 The Long Short-Term Memory Architecture. 37 2.3.3 Gated Recurrent Units. 39 2.3.4 Prosody Prediction Models 40 Chapter 3. Vowel Duration and Fundamental Frequency Prediction Model 43 3.1 Data 43 3.2. Input Feature Selection. 45 3.3 System Architecture and Training 56 3.4 Results and Evaluation 63 3.4.1 Objective Metrics. 63 3.4.2 Vowel Duration Prediction Models Results. 65 3.4.2 Fundamental Frequency Prediction Models Results 68 3.4.3 Comparison with other models . 68 Chapter 4. Automatic Prosody Transplantation 72 4.1 Data 72 4.2 Transplantation Method. 74 4.3 Perceptual Evaluation 79 4.4 Results 80 Chapter 5. Conclusion. 82 5.1 Summary 82 5.2 Contribution 84 5.3 Limitations 85 5.4 Recommendations for Future Study. 85 References 88 Appendix 96Maste

A dynamic deep learning approach for intonation modeling

Intonation plays a crucial role in making synthetic speech sound more natural. However, intonation modeling largely remains an open question. In my thesis, the interpolated F0 is parameterized dynamically by means of sign values, encoding the direction of pitch change, and corresponding quantized magnitude values, encoding the amount of pitch change in such direction. The sign and magnitude values are used for the training of a dedicated neural network. The proposed methodology is evaluated and compared to a state-of-the-art DNN-based TTS system. To this end, a segmental synthesizer was implemented to normalize the effect of the spectrum. The synthesizer uses the F0 and linguistic features to predict the spectrum, aperiodicity, and voicing information. The proposed methodology performs as well as the reference system, and we observe a trend for native speakers to prefer the proposed intonation model

English Lexical Stress Recognition Using Recurrent Neural Networks

Lexical stress is an integral part of English pronunciation. The command of lexical stress has an effect on the perceived fluency of the speaker. Moreover, it serves as a cue to recognize words. Methods that can automatically recognize lexical stress in spoken audio can be used to help English learners improve their pronunciation. This thesis evaluated lexical stress recognition methods based on recurrent neural networks. The purpose was to compare two sets of features: a set of prosodic features making use of existing speech recognition technologies, and simple spectral features. Using the latter feature set would allow for an end-to-end model, significantly simplifying the overall process. The problem was formulated as one of locating the primary stress, the most prominently stressed syllable in the word, in an isolated word. Datasets of both native and non-native speech were used in the experiments. The results show that models using the prosodic features outperform models using the spectral features. The difference between the two was particularly stark on the non-native dataset. It is possible that the datasets were too small to enable training end-to-end models. There was a considerable variation in performance among different words. It was also observed that the presence of a secondary stress made it more difficult to detect the primary stress.Sanapaino on olennainen osa englannin kielen ääntämistä. Sen osaaminen vaikuttaa puhujan havaittuun sujuvuuteen, ja se toimii vihjeenä sanojen tunnistamiselle. Menetelmiä, joilla sanapaino voidaan automaattisesti tunnistaa puheesta, voidaan käyttää apuna englannin oppijoiden ääntämisen parantamisessa. Tämä diplomityö arvioi takaisinkytkeytyviin neuroverkkoihin perustuvia menetelmiä sanapainon tunnistukseen. Tarkoitus oli vertailla kahdenlaisia piirteitä: joukkoa prosodisia piirteitä, jotka hyödyntävät olemassa olevia puheentunnistusteknologioita, ja yksinkertaisia äänen spektriin perustuvia piirteitä. Jälkimmäisten piirteiden käyttö mahdollistaisi päästä-päähän -mallien käyttämisen, mikä yksinkertaistaisi kokonaisprosessia merkittävästi. Ongelma esitettiin muodossa, jossa tarkoitus oli löytää pääpainon sijainti, eli sanan voimakkaiten erottuva tavu, yksittäisestä sanasta. Tutkimuksessa käytettiin dataa sekä englantia äidinkielenään että ei-äidinkielenään puhuvilta. Tulosten mukaan prosodisia piirteitä käyttävät mallit suoriutuvat tehtävästä paremmin kuin äänen spektriin perustuvia piirteitä käyttävät mallit. Erot olivat erityisen suuria datajoukossa, joka koostui englantia ei-äidinkielenään puhuvien puheesta. On mahdollista, että käytetyt datajoukot olivat liian pieniä päästä-päähän -mallien opettamista varten. Mallien suorituskyvyssä oli huomattavaa vaihtelua eri sanojen välillä. Tutkimuksessa havaittiin myös, että sivupainon läsnäolo vaikeutti pääpainon tunnistamista