Analysis of Unsupervised and Noise-Robust Speaker-Adaptive HMM-Based Speech Synthesis Systems toward a Unified ASR and TTS Framework

For the 2009 Blizzard Challenge we have built an unsupervised version of the HTS-2008 speaker-adaptive HMM-based speech synthesis system for English, and a noise robust version of the systems for Mandarin. They are designed from a multidisciplinary application point of view in that we attempt to integrate the components of the TTS system with other technologies such as ASR. All the average voice models are trained exclusively from recognized, publicly available, ASR databases. Multi-pass LVCSR and confidence scores calculated from confusion network are used for the unsupervised systems, and noisy data recorded in cars or public spaces is used for the noise robust system. We believe the developed systems form solid benchmarks and provide good connections to ASR fields. This paper describes the development of the systems and reports the results and analysis of their evaluation

Voice source characterization for prosodic and spectral manipulation

The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase. In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters reported in the literature, complemented with our own results from the vowel database. The results show that our method gives satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good). Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in order to achieve quality levels similar to the reference methods. As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters extracted using our algorithm have a positive impact in the field of automatic emotion classification

CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES

Tesis por compendio[ES] Durante los últimos años, los repositorios multimedia en línea se han convertido en fuentes clave de conocimiento gracias al auge de Internet, especialmente en el área de la educación. Instituciones educativas de todo el mundo han dedicado muchos recursos en la búsqueda de nuevos métodos de enseñanza, tanto para mejorar la asimilación de nuevos conocimientos, como para poder llegar a una audiencia más amplia. Como resultado, hoy en día disponemos de diferentes repositorios con clases grabadas que siven como herramientas complementarias en la enseñanza, o incluso pueden asentar una nueva base en la enseñanza a distancia. Sin embargo, deben cumplir con una serie de requisitos para que la experiencia sea totalmente satisfactoria y es aquí donde la transcripción de los materiales juega un papel fundamental. La transcripción posibilita una búsqueda precisa de los materiales en los que el alumno está interesado, se abre la puerta a la traducción automática, a funciones de recomendación, a la generación de resumenes de las charlas y además, el poder hacer llegar el contenido a personas con discapacidades auditivas. No obstante, la generación de estas transcripciones puede resultar muy costosa. Con todo esto en mente, la presente tesis tiene como objetivo proporcionar nuevas herramientas y técnicas que faciliten la transcripción de estos repositorios. En particular, abordamos el desarrollo de un conjunto de herramientas de reconocimiento de automático del habla, con énfasis en las técnicas de aprendizaje profundo que contribuyen a proporcionar transcripciones precisas en casos de estudio reales. Además, se presentan diferentes participaciones en competiciones internacionales donde se demuestra la competitividad del software comparada con otras soluciones. Por otra parte, en aras de mejorar los sistemas de reconocimiento, se propone una nueva técnica de adaptación de estos sistemas al interlocutor basada en el uso Medidas de Confianza. Esto además motivó el desarrollo de técnicas para la mejora en la estimación de este tipo de medidas por medio de Redes Neuronales Recurrentes. Todas las contribuciones presentadas se han probado en diferentes repositorios educativos. De hecho, el toolkit transLectures-UPV es parte de un conjunto de herramientas que sirve para generar transcripciones de clases en diferentes universidades e instituciones españolas y europeas.[CA] Durant els últims anys, els repositoris multimèdia en línia s'han convertit en fonts clau de coneixement gràcies a l'expansió d'Internet, especialment en l'àrea de l'educació. Institucions educatives de tot el món han dedicat molts recursos en la recerca de nous mètodes d'ensenyament, tant per millorar l'assimilació de nous coneixements, com per poder arribar a una audiència més àmplia. Com a resultat, avui dia disposem de diferents repositoris amb classes gravades que serveixen com a eines complementàries en l'ensenyament, o fins i tot poden assentar una nova base a l'ensenyament a distància. No obstant això, han de complir amb una sèrie de requisits perquè la experiència siga totalment satisfactòria i és ací on la transcripció dels materials juga un paper fonamental. La transcripció possibilita una recerca precisa dels materials en els quals l'alumne està interessat, s'obri la porta a la traducció automàtica, a funcions de recomanació, a la generació de resums de les xerrades i el poder fer arribar el contingut a persones amb discapacitats auditives. No obstant, la generació d'aquestes transcripcions pot resultar molt costosa. Amb això en ment, la present tesi té com a objectiu proporcionar noves eines i tècniques que faciliten la transcripció d'aquests repositoris. En particular, abordem el desenvolupament d'un conjunt d'eines de reconeixement automàtic de la parla, amb èmfasi en les tècniques d'aprenentatge profund que contribueixen a proporcionar transcripcions precises en casos d'estudi reals. A més, es presenten diferents participacions en competicions internacionals on es demostra la competitivitat del programari comparada amb altres solucions. D'altra banda, per tal de millorar els sistemes de reconeixement, es proposa una nova tècnica d'adaptació d'aquests sistemes a l'interlocutor basada en l'ús de Mesures de Confiança. A més, això va motivar el desenvolupament de tècniques per a la millora en l'estimació d'aquest tipus de mesures per mitjà de Xarxes Neuronals Recurrents. Totes les contribucions presentades s'han provat en diferents repositoris educatius. De fet, el toolkit transLectures-UPV és part d'un conjunt d'eines que serveix per generar transcripcions de classes en diferents universitats i institucions espanyoles i europees.[EN] During the last years, on-line multimedia repositories have become key knowledge assets thanks to the rise of Internet and especially in the area of education. Educational institutions around the world have devoted big efforts to explore different teaching methods, to improve the transmission of knowledge and to reach a wider audience. As a result, online video lecture repositories are now available and serve as complementary tools that can boost the learning experience to better assimilate new concepts. In order to guarantee the success of these repositories the transcription of each lecture plays a very important role because it constitutes the first step towards the availability of many other features. This transcription allows the searchability of learning materials, enables the translation into another languages, provides recommendation functions, gives the possibility to provide content summaries, guarantees the access to people with hearing disabilities, etc. However, the transcription of these videos is expensive in terms of time and human cost. To this purpose, this thesis aims at providing new tools and techniques that ease the transcription of these repositories. In particular, we address the development of a complete Automatic Speech Recognition Toolkit with an special focus on the Deep Learning techniques that contribute to provide accurate transcriptions in real-world scenarios. This toolkit is tested against many other in different international competitions showing comparable transcription quality. Moreover, a new technique to improve the recognition accuracy has been proposed which makes use of Confidence Measures, and constitutes the spark that motivated the proposal of new Confidence Measures techniques that helped to further improve the transcription quality. To this end, a new speaker-adapted confidence measure approach was proposed for models based on Recurrent Neural Networks. The contributions proposed herein have been tested in real-life scenarios in different educational repositories. In fact, the transLectures-UPV toolkit is part of a set of tools for providing video lecture transcriptions in many different Spanish and European universities and institutions.Agua Teba, MÁD. (2019). CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130198TESISCompendi

Phonological vocoding using artificial neural networks

We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The speech reconstruction process involves using a Deep Neural Network (DNN) to map phonological features posteriors to speech parameters -- line spectra and glottal signal parameters -- followed by LPC resynthesis. This DNN is trained on a target voice without transcriptions, in a semi-supervised manner. Both encoder and decoder are based on neural networks and thus the vocoding is achieved using a simple fast forward pass. An experiment with French vocoding and a target male voice trained on 21 hour long audio book is presented. An application of the phonological vocoder to low bit rate speech coding is shown, where transmitted phonological posteriors are pruned and quantized. The vocoder with scalar quantization operates at 1 kbps, with potential for lower bit-rate

Acoustic Data-Driven Grapheme-to-Phoneme Conversion in the Probabilistic Lexical Modeling Framework

One of the primary steps in building automatic speech recognition (ASR) and text-to-speech systems is the development of a phonemic lexicon that provides a mapping between each word and its pronunciation as a sequence of phonemes. Phoneme lexicons can be developed by humans through use of linguistic knowledge, however, this would be a costly and time-consuming task. To facilitate this process, grapheme-to phoneme conversion (G2P) techniques are used in which, given an initial phoneme lexicon, the relationship between graphemes and phonemes is learned through data-driven methods. This article presents a novel G2P formalism which learns the grapheme-to-phoneme relationship through acoustic data and potentially relaxes the need for an initial phonemic lexicon in the target language. The formalism involves a training part followed by an inference part. In the training part, the grapheme-to-phoneme relationship is captured in a probabilistic lexical modeling framework. In this framework, a hidden Markov model (HMM) is trained in which each HMM state representing a grapheme is parameterized by a categorical distribution of phonemes. Then in the inference part, given the orthographic transcription of the word and the learned HMM, the most probable sequence of phonemes is inferred. In this article, we show that the recently proposed acoustic G2P approach in the Kullback Leibler divergence-based HMM (KL-HMM) framework is a particular case of this formalism. We then benchmark the approach against two popular G2P approaches, namely joint multigram approach and decision tree-based approach. Our experimental studies on English and French show that despite relatively poor performance at the pronunciation level, the performance of the proposed approach is not significantly different than the state-of-the-art G2P methods at the ASR level. (C) 2016 Elsevier B.V. All rights reserved

Building well-performing classifier ensembles: model and decision level combination.

There is a continuing drive for better, more robust generalisation performance from classification systems, and prediction systems in general. Ensemble methods, or the combining of multiple classifiers, have become an accepted and successful tool for doing this, though the reasons for success are not always entirely understood. In this thesis, we review the multiple classifier literature and consider the properties an ensemble of classifiers - or collection of subsets - should have in order to be combined successfully. We find that the framework of Stochastic Discrimination provides a well-defined account of these properties, which are shown to be strongly encouraged in a number of the most popular/successful methods in the literature via differing algorithmic devices. This uncovers some interesting and basic links between these methods, and aids understanding of their success and operation in terms of a kernel induced on the training data, with form particularly well suited to classification. One property that is desirable in both the SD framework and in a regression context, the ambiguity decomposition of the error, is de-correlation of individuals. This motivates the introduction of the Negative Correlation Learning method, in which neural networks are trained in parallel in a way designed to encourage de-correlation of the individual networks. The training is controlled by a parameter λ governing the extent to which correlations are penalised. Theoretical analysis of the dynamics of training results in an exact expression for the interval in which we can choose λ while ensuring stability of the training, and a value λ∗ for which the training has some interesting optimality properties. These values depend only on the size N of the ensemble. Decision level combination methods often result in a difficult to interpret model, and NCL is no exception. However in some applications, there is a need for understandable decisions and interpretable models. In response to this, we depart from the standard decision level combination paradigm to introduce a number of model level combination methods. As decision trees are one of the most interpretable model structures used in classification, we chose to combine structure from multiple individual trees to build a single combined model. We show that extremely compact, well performing models can be built in this way. In particular, a generalisation of bottom-up pruning to a multiple-tree context produces good results in this regard. Finally, we develop a classification system for a real-world churn prediction problem, illustrating some of the concepts introduced in the thesis, and a number of more practical considerations which are of importance when developing a prediction system for a specific problem

Advances in the application of support vector machines as probabilistic estimators for continuous automatic speech recognition

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, noviembre de 200