111 research outputs found
Singing speaker clustering based on subspace learning in the GMM mean supervector space
Abstract In this study, we propose algorithms based on subspace learning in the GMM mean supervector space to improve performance of speaker clustering with speech from both reading and singing. As a speaking style, singing introduces changes in the time-frequency structure of a speaker's voice. The purpose of this study is to introduce advancements for speech systems such as speech indexing and retrieval which improve robustness to intrinsic variations in speech production. Speaker clustering techniques such as k-means and hierarchical are explored for analysis of acoustic space differences of a corpus consisting of reading and singing of lyrics for each speaker. Furthermore, a distance based on fuzzy c-means membership degrees is proposed to more accurately measure clustering difficulty or speaker confusability. Two categories of subspace learning methods are studied: unsupervised based on LPP, and supervised based on PLDA. Our proposed clustering method based on PLDA is a two stage algorithm: where first, initial clusters are obtained using full dimension supervectors, and next, each cluster is refined in a PLDA subspace resulting in a more speaker dependent representation that is less sensitive to speaking style. It is shown that LPP improves average clustering accuracy by 5.1% absolute versus a hierarchical baseline for a mixture of reading and singing, and PLDA based clustering increases accuracy by 9.6% absolute versus a k-means baseline. The advancements offer novel techniques to improve model formulation for speech applications including speaker ID, audio search, and audio content analysis
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
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
An exploration of the rhythm of Malay
In recent years there has been a surge of interest in speech rhythm. However we still lack a clear understanding of the nature of rhythm and rhythmic differences across languages. Various metrics have been proposed as means for measuring rhythm on the phonetic level and making typological comparisons between languages (Ramus et al, 1999; Grabe & Low, 2002; Dellwo, 2006) but the debate is ongoing on the extent to which these metrics capture the rhythmic basis of speech (Arvaniti, 2009; Fletcher, in press). Furthermore, cross linguistic studies of rhythm have covered a relatively small number of languages and research on previously unclassified languages is necessary to fully develop the typology of rhythm. This study examines the rhythmic features of Malay, for which, to date, relatively little work has been carried out on aspects rhythm and timing.
The material for the analysis comprised 10 sentences produced by 20 speakers of standard Malay (10 males and 10 females). The recordings were first analysed using rhythm metrics proposed by Ramus et. al (1999) and Grabe & Low (2002). These metrics (∆C, %V, rPVI, nPVI) are based on durational measurements of vocalic and consonantal intervals. The results indicated that Malay clustered with other so-called syllable-timed languages like French and Spanish on the basis of all metrics. However, underlying the overall findings for these metrics there was a large degree of variability in values across speakers and sentences, with some speakers having values in the range typical of stressed-timed languages like English.
Further analysis has been carried out in light of Fletcher’s (in press) argument that measurements based on duration do not wholly reflect speech rhythm as there are many other factors that can influence values of consonantal and vocalic intervals, and Arvaniti’s (2009) suggestion that other features of speech should also be considered in description of rhythm to discover what contributes to listeners’ perception of regularity. Spectrographic analysis of the Malay recordings brought to light two parameters that displayed consistency and regularity for all speakers and sentences: the duration of individual vowels and the duration of intervals between intensity minima.
This poster presents the results of these investigations and points to connections between the features which seem to be consistently regulated in the timing of Malay connected speech and aspects of Malay phonology. The results are discussed in light of current debate on the descriptions of rhythm
Estudio y mejora de sistemas de verificación de locutores bajo condiciones de voz afónica
Este proyecto analiza las diferencias entre distintos modos del habla, en especial el susurro, utilizado como medio de comunicación cuando se padece una enfermedad como la afonı́a, y cómo afectan a los sistemas de verificación automática de locutores. El objetivo del proyecto es el estudio de la pérdida de prestaciones, y la mejora de los sistemas mediante la aplicación de distintas técnicas.El estudio parte del análisis de las señales en los dominios de voz susurrada y voz neutra, cuyas diferencias explican el detrimento del sistema. Para cuantificarlo, se escogen un sistema de referencia de altas prestaciones y una base de datos que cuenta con audios en condiciones de habla normal y susurrada.Las técnicas de mejora estudiadas abordan el problema en distintos puntos del sistema completo. Estas técnicas se introducen de forma teórica en el segundo bloque del trabajo, y en el tercer bloque se muestran los resultados obtenidos para cada una de ellas. Para evaluarlas y compararlas se utilizan herramientas de software libre, herramientas de visualización y entrenamiento de modelos estadı́sticos utilizando Python como lenguaje de programación principal. El trabajo muestra el rendimiento de herramientas alternativas a los algoritmos populares de aprendizaje automático, necesarias cuando no se dispone de una cantidad significativa de datos que permitan buenos resultados.<br /
Interfaces de fala silenciosa multimodais para português europeu com base na articulação
Doutoramento conjunto MAPi em InformáticaThe concept of silent speech, when applied to Human-Computer Interaction (HCI), describes a system which allows for speech communication in the absence of an acoustic signal. By analyzing data gathered during different parts of the human speech production process, Silent Speech Interfaces (SSI) allow users with speech impairments to communicate with a system. SSI can also be used in the presence of environmental noise, and in situations in which privacy, confidentiality, or non-disturbance are important.
Nonetheless, despite recent advances, performance and usability of Silent Speech systems still have much room for improvement. A better performance of such systems would enable their application in relevant areas, such as Ambient Assisted Living. Therefore, it is necessary to extend our understanding of the capabilities and limitations of silent speech modalities and to enhance their joint exploration.
Thus, in this thesis, we have established several goals: (1) SSI language expansion to support European Portuguese; (2) overcome identified limitations of current SSI techniques to detect EP nasality (3) develop a Multimodal HCI approach for SSI based on non-invasive modalities; and (4) explore more direct measures in the Multimodal SSI for EP acquired from more invasive/obtrusive modalities, to be used as ground truth in articulation processes, enhancing our comprehension of other modalities.
In order to achieve these goals and to support our research in this area, we have created a multimodal SSI framework that fosters leveraging modalities and combining information, supporting research in multimodal SSI. The proposed framework goes beyond the data acquisition process itself, including methods for online and offline synchronization, multimodal data processing, feature extraction, feature selection, analysis, classification and prototyping. Examples of applicability are provided for each stage of the framework. These include articulatory studies for HCI, the development of a multimodal SSI based on less invasive modalities and the use of ground truth information coming from more invasive/obtrusive modalities to overcome the limitations of other modalities.
In the work here presented, we also apply existing methods in the area of SSI to EP for the first time, noting that nasal sounds may cause an inferior performance in some modalities. In this context, we propose a non-invasive solution for the detection of nasality based on a single Surface Electromyography sensor, conceivable of being included in a multimodal SSI.O conceito de fala silenciosa, quando aplicado a interação humano-computador, permite a comunicação na ausência de um sinal acústico. Através da análise de dados, recolhidos no processo de produção de fala humana, uma interface de fala silenciosa (referida como SSI, do inglês Silent Speech Interface) permite a utilizadores com deficiências ao nível da fala comunicar com um sistema. As SSI podem também ser usadas na presença de ruído ambiente, e em situações em que privacidade, confidencialidade, ou não perturbar, é importante.
Contudo, apesar da evolução verificada recentemente, o desempenho e usabilidade de sistemas de fala silenciosa tem ainda uma grande margem de progressão. O aumento de desempenho destes sistemas possibilitaria assim a sua aplicação a áreas como Ambientes Assistidos. É desta forma fundamental alargar o nosso conhecimento sobre as capacidades e limitações das modalidades utilizadas para fala silenciosa e fomentar a sua exploração conjunta.
Assim, foram estabelecidos vários objetivos para esta tese: (1) Expansão das linguagens suportadas por SSI com o Português Europeu; (2) Superar as limitações de técnicas de SSI atuais na deteção de nasalidade; (3) Desenvolver uma abordagem SSI multimodal para interação humano-computador, com base em modalidades não invasivas; (4) Explorar o uso de medidas diretas e complementares, adquiridas através de modalidades mais invasivas/intrusivas em configurações multimodais, que fornecem informação exata da articulação e permitem aumentar a nosso entendimento de outras modalidades.
Para atingir os objetivos supramencionados e suportar a investigação nesta área procedeu-se à criação de uma plataforma SSI multimodal que potencia os meios para a exploração conjunta de modalidades. A plataforma proposta vai muito para além da simples aquisição de dados, incluindo também métodos para sincronização de modalidades, processamento de dados multimodais, extração e seleção de características, análise, classificação e prototipagem. Exemplos de aplicação para cada fase da plataforma incluem: estudos articulatórios para interação humano-computador, desenvolvimento de uma SSI multimodal com base em modalidades não invasivas, e o uso de informação exata com origem em modalidades invasivas/intrusivas para superar limitações de outras modalidades.
No trabalho apresentado aplica-se ainda, pela primeira vez, métodos retirados do estado da arte ao Português Europeu, verificando-se que sons nasais podem causar um desempenho inferior de um sistema de fala silenciosa. Neste contexto, é proposta uma solução para a deteção de vogais nasais baseada num único sensor de eletromiografia, passível de ser integrada numa interface de fala silenciosa multimodal
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
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