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

Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this book is substantially improved in terms of accuracy, flexibility, and robustness

Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this book is substantially improved in terms of accuracy, flexibility, and robustness

Deep neural network techniques for monaural speech enhancement: state of the art analysis

Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain. More specifically, DNN models have been applied in speech enhancement domain to achieve denosing, dereverberation and multi-speaker separation in monaural speech enhancement. In this paper, we review some dominant DNN techniques being employed to achieve speech separation. The review looks at the whole pipeline of speech enhancement from feature extraction, how DNN based tools are modelling both global and local features of speech and model training (supervised and unsupervised). We also review the use of speech-enhancement pre-trained models to boost speech enhancement process. The review is geared towards covering the dominant trends with regards to DNN application in speech enhancement in speech obtained via a single speaker.Comment: conferenc

Features of hearing: applications of machine learning to uncover the building blocks of hearing

Recent advances in machine learning have instigated a renewed interest in using machine learning approaches to better understand human sensory processing. This line of research is particularly interesting for speech research since speech comprehension is uniquely human, which complicates obtaining detailed neural recordings. In this thesis, I explore how machine learning can be used to uncover new knowledge about the auditory system, with a focus on discovering robust auditory features. The resulting increased understanding of the noise robustness of human hearing may help to better assist those with hearing loss and improve Automatic Speech Recognition (ASR) systems. First, I show how computational neuroscience and machine learning can be combined to generate hypotheses about auditory features. I introduce a neural feature detection model with a modest number of parameters that is compatible with auditory physiology. By testing feature detector variants in a speech classification task, I confirm the importance of both well-studied and lesser-known auditory features. Second, I investigate whether ASR software is a good candidate model of the human auditory system. By comparing several state-of-the-art ASR systems to the results from humans on a range of psychometric experiments, I show that these ASR systems diverge markedly from humans in at least some psychometric tests. This implies that none of these systems act as a strong proxy for human speech recognition, although some may be useful when asking more narrowly defined questions. For neuroscientists, this thesis exemplifies how machine learning can be used to generate new hypotheses about human hearing, while also highlighting the caveats of investigating systems that may work fundamentally differently from the human brain. For machine learning engineers, I point to tangible directions for improving ASR systems. To motivate the continued cross-fertilization between these fields, a toolbox that allows researchers to assess new ASR systems has been released.Open Acces

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis