Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Temporally Varying Weight Regression for Speech Recognition

Ph.DDOCTOR OF PHILOSOPH

Automatic Speech Recognition for Documenting Endangered First Nations Languages

Automatic speech recognition (ASR) for low-resource languages is an active field of research. Over the past years with the advent of deep learning, impressive achievements have been reported using minimal resources. As many of the world’s languages are getting extinct every year, with every dying language we lose intellect, culture, values, and tradition which generally pass down for long generations. Linguists throughout the world have already initiated many projects on language documentation to preserve such endangered languages. Automatic speech recognition is a solution to accelerate the documentation process reducing the annotation time for field linguists as well as the overall cost of the project. A traditional speech recognizer is trained on thousands of hours of acoustic data and a phonetic dictionary that includes all words from the language. End-to-End ASR systems have shown dramatic improvement for major languages. Especially, recent advancement in self-supervised representation learning which takes advantage of large corpora of untranscribed speech data has become the state-of-the-art for speech recognition technology. However, for resource-constrained languages, the technology is not tested in depth. In this thesis, we explore both traditional methods of ASR and state-of-the-art end-to-end systems for modeling a critically endangered Athabascan language known as Upper Tanana. In our first approach, we investigate traditional models with a comparative study on feature selection and a performance comparison with deep hybrid models. With limited resources at our disposal, we build a working ASR system based on a grapheme-to-phoneme (G2P) phonetic dictionary. The acoustic model can also be used as a separate forced alignment tool for the automatic alignment of training data. The results show that the GMM-HMM methods outperform deep hybrid models in low-resource acoustic modeling. In our second approach, we propose using Domain-adapted Cross-lingual Speech Recognition (DA-XLSR) for an ASR system, developed over the wav2vec 2.0 framework that utilizes pretrained transformer models leveraging cross lingual data for building an acoustic representation. The proposed system uses a multistage transfer learning process in order to fine tune the final model. To supplement the limited data, we compile a data augmentation strategy combining six augmentation techniques. The speech model uses Connectionist Temporal Classification (CTC) for an alignment free training and does not require any pronunciation dictionary or language model. Experiments from the second approach demonstrate that it can outperform the best traditional or end-to-end models in terms of word error rate (WER) and produce a powerful utterance level transcription. On top of that, the augmentation strategy is tested on several end-to-end models, and it provides a consistent improvement in performance. While the best proposed model can currently reduce the WER significantly, it may still require further research to completely replace the need for human transcribers

A System for Simultaneous Translation of Lectures and Speeches

This thesis realizes the first existing automatic system for simultaneous speech-to-speech translation. The focus of this system is the automatic translation of (technical oriented) lectures and speeches from English to Spanish, but the different aspects described in this thesis will also be helpful for developing simultaneous translation systems for other domains or languages

GREC: Multi-domain Speech Recognition for the Greek Language

Μία από τις κορυφαίες προκλήσεις στην Αυτόματη Αναγνώριση Ομιλίας είναι η ανάπτυξη ικανών συστημάτων που μπορούν να έχουν ισχυρή απόδοση μέσα από διαφορετικές συνθήκες ηχογράφησης. Στο παρόν έργο κατασκευάζουμε και αναλύουμε το GREC, μία μεγάλη πολυτομεακή συλλογή δεδομένων για αυτόματη αναγνώριση ομιλίας στην ελληνική γλώσσα. Το GREC αποτελείται από τρεις βάσεις δεδομένων στους θεματικούς τομείς των «εκπομπών ειδήσεων», «ομιλίας από δωρισμένες εγγραφές φωνής», «ηχητικών βιβλίων» και μιας νέας συλλογής δεδομένων στον τομέα των «πολιτικών ομιλιών». Για τη δημιουργία του τελευταίου, συγκεντρώνουμε δεδομένα ομιλίας από ηχογραφήσεις των επίσημων συνεδριάσεων της Βουλής των Ελλήνων, αποδίδοντας ένα σύνολο δεδομένων που αποτελείται από 120 ώρες ομιλίας πολιτικού περιεχομένου. Περιγράφουμε με λεπτομέρεια την καινούρια συλλογή δεδομένων, την προεπεξεργασία και την ευθυγράμμιση ομιλίας, τα οποία βασίζονται στο εργαλείο ανοιχτού λογισμικού Kaldi. Επιπλέον, αξιολογούμε την απόδοση των μοντέλων Gaussian Mixture (GMM) - Hidden Markov (HMM) και Deep Neural Network (DNN) - HMM όταν εφαρμόζονται σε δεδομένα από διαφορετικούς τομείς. Τέλος, προσθέτουμε τη δυνατότητα αυτόματης δεικτοδότησης ομιλητών στο Kaldi-gRPC-Server, ενός εργαλείου γραμμένο σε Python που βασίζεται στο PyKaldi και στο gRPC για βελτιωμένη ανάπτυξη μοντέλων αυτόματης αναγνώρισης ομιλίας.One of the leading challenges in Automatic Speech Recognition (ASR) is the development of robust systems that can perform well under multiple settings. In this work we construct and analyze GREC, a large, multi-domain corpus for automatic speech recognition for the Greek language. GREC is a collection of three available subcorpora over the domains of “news casts”, “crowd-sourced speech”, “audiobooks”, and a new corpus in the domain of “public speeches”. For the creation of the latter, HParl, we collect speech data from recordings of the official proceedings of the Hellenic Parliament, yielding, a dataset which consists of 120 hours of political speech segments. We describe our data collection, pre-processing and alignment setup, which are based on Kaldi toolkit. Furthermore, we perform extensive ablations on the recognition performance of Gaussian Mixture (GMM) - Hidden Markov (HMM) models and Deep Neural Network (DNN) - HMM models over the different domains. Finally, we integrate speaker diarization features to Kaldi-gRPC-Server, a modern, pythonic tool based on PyKaldi and gRPC for streamlined deployment of Kaldi based speech recognition

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

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

Multi-level acoustic modeling for automatic speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 183-192).Context-dependent acoustic modeling is commonly used in large-vocabulary Automatic Speech Recognition (ASR) systems as a way to model coarticulatory variations that occur during speech production. Typically, the local phoneme context is used as a means to define context-dependent units. Because the number of possible context-dependent units can grow exponentially with the length of the contexts, many units will not have enough training examples to train a robust model, resulting in a data sparsity problem. For nearly two decades, this data sparsity problem has been dealt with by a clustering-based framework which systematically groups different context-dependent units into clusters such that each cluster can have enough data. Although dealing with the data sparsity issue, the clustering-based approach also makes all context-dependent units within a cluster have the same acoustic score, resulting in a quantization effect that can potentially limit the performance of the context-dependent model. In this work, a multi-level acoustic modeling framework is proposed to address both the data sparsity problem and the quantization effect. Under the multi-level framework, each context-dependent unit is associated with classifiers that target multiple levels of contextual resolution, and the outputs of the classifiers are linearly combined for scoring during recognition. By choosing the classifiers judiciously, both the data sparsity problem and the quantization effect can be dealt with. The proposed multi-level framework can also be integrated into existing large-vocabulary ASR systems, such as FST-based ASR systems, and is compatible with state-of-the-art error reduction techniques for ASR systems, such as discriminative training methods. Multiple sets of experiments have been conducted to compare the performance of the clustering-based acoustic model and the proposed multi-level model. In a phonetic recognition experiment on TIMIT, the multi-level model has about 8% relative improvement in terms of phone error rate, showing that the multi-level framework can help improve phonetic prediction accuracy. In a large-vocabulary transcription task, combining the proposed multi-level modeling framework with discriminative training can provide more than 20% relative improvement over a clustering baseline model in terms of Word Error Rate (WER), showing that the multi-level framework can be integrated into existing large-vocabulary decoding frameworks and that it combines well with discriminative training methods. In speaker adaptive transcription task, the multi-level model has about 14% relative WER improvement, showing that the proposed framework can adapt better to new speakers, and potentially to new environments than the conventional clustering-based approach.by Hung-An Chang.Ph.D

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli