Automatic Pronunciation Assessment -- A Review

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding

The SP2 SCOPES Project on Speech Prosody

This is an overview of a Joint Research Project within the Scientific co-operation between Eastern Europe and Switzerland (SCOPES) Program of the Swiss National Science Foundation (SNFS) and Swiss Agency for Development and Cooperation (SDC). Within the SP2 SCOPES Project on Speech Prosody, in the course of the following two years, the four partners aim to collaborate on the subject of speech prosody and advance the extraction, processing, modeling and transfer of prosody for a large portfolio of European languages: French, German, Italian, English, Hungarian, Serbian, Croatian, Bosnian, Montenegrin, and Macedonian. Through the intertwined four research plans, synergies are foreseen to emerge that will build a foundation for submitting strong joint proposals for EU funding

Generation of prosody and speech for Mandarin Chinese

Ph.DDOCTOR OF PHILOSOPH

Suprasegmental representations for the modeling of fundamental frequency in statistical parametric speech synthesis

Statistical parametric speech synthesis (SPSS) has seen improvements over recent years, especially in terms of intelligibility. Synthetic speech is often clear and understandable, but it can also be bland and monotonous. Proper generation of natural speech prosody is still a largely unsolved problem. This is relevant especially in the context of expressive audiobook speech synthesis, where speech is expected to be fluid and captivating. In general, prosody can be seen as a layer that is superimposed on the segmental (phone) sequence. Listeners can perceive the same melody or rhythm in different utterances, and the same segmental sequence can be uttered with a different prosodic layer to convey a different message. For this reason, prosody is commonly accepted to be inherently suprasegmental. It is governed by longer units within the utterance (e.g. syllables, words, phrases) and beyond the utterance (e.g. discourse). However, common techniques for the modeling of speech prosody - and speech in general - operate mainly on very short intervals, either at the state or frame level, in both hidden Markov model (HMM) and deep neural network (DNN) based speech synthesis. This thesis presents contributions supporting the claim that stronger representations of suprasegmental variation are essential for the natural generation of fundamental frequency for statistical parametric speech synthesis. We conceptualize the problem by dividing it into three sub-problems: (1) representations of acoustic signals, (2) representations of linguistic contexts, and (3) the mapping of one representation to another. The contributions of this thesis provide novel methods and insights relating to these three sub-problems. In terms of sub-problem 1, we propose a multi-level representation of f0 using the continuous wavelet transform and the discrete cosine transform, as well as a wavelet-based decomposition strategy that is linguistically and perceptually motivated. In terms of sub-problem 2, we investigate additional linguistic features such as text-derived word embeddings and syllable bag-of-phones and we propose a novel method for learning word vector representations based on acoustic counts. Finally, considering sub-problem 3, insights are given regarding hierarchical models such as parallel and cascaded deep neural networks

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

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

Statistical morphological disambiguation with application to disambiguation of pronunciations in Turkish /

The statistical morphological disambiguation of agglutinative languages suffers from data sparseness. In this study, we introduce the notion of distinguishing tag sets (DTS) to overcome the problem. The morphological analyses of words are modeled with DTS and the root major part-of-speech tags. The disambiguator based on the introduced representations performs the statistical morphological disambiguation of Turkish with a recall of as high as 95.69 percent. In text-to-speech systems and in developing transcriptions for acoustic speech data, the problem occurs in disambiguating the pronunciation of a token in context, so that the correct pronunciation can be produced or the transcription uses the correct set of phonemes. We apply the morphological disambiguator to this problem of pronunciation disambiguation and achieve 99.54 percent recall with 97.95 percent precision. Most text-to-speech systems perform phrase level accentuation based on content word/function word distinction. This approach seems easy and adequate for some right headed languages such as English but is not suitable for languages such as Turkish. We then use a a heuristic approach to mark up the phrase boundaries based on dependency parsing on a basis of phrase level accentuation for Turkish TTS synthesizers