The Unsupervised Acquisition of a Lexicon from Continuous Speech

We present an unsupervised learning algorithm that acquires a natural-language lexicon from raw speech. The algorithm is based on the optimal encoding of symbol sequences in an MDL framework, and uses a hierarchical representation of language that overcomes many of the problems that have stymied previous grammar-induction procedures. The forward mapping from symbol sequences to the speech stream is modeled using features based on articulatory gestures. We present results on the acquisition of lexicons and language models from raw speech, text, and phonetic transcripts, and demonstrate that our algorithm compares very favorably to other reported results with respect to segmentation performance and statistical efficiency.Comment: 27 page technical repor

Incorporating Weak Statistics for Low-Resource Language Modeling

Automatic speech recognition (ASR) requires a strong language model to guide the acoustic model and favor likely utterances. While many tasks enjoy billions of language model training tokens, many domains which require ASR do not have readily available electronic corpora.The only source of useful language modeling data is expensive and time-consuming human transcription of in-domain audio. This dissertation seeks to quickly and inexpensively improve low-resource language modeling for use in automatic speech recognition. This dissertation first considers efficient use of non-professional human labor to best improve system performance, and demonstrate that it is better to collect more data, despite higher transcription error, than to redundantly transcribe data to improve quality. In the process of developing procedures to collect such data, this work also presents an efficient rating scheme to detect poor transcribers without gold standard data. As an alternative to this process, automatic transcripts are generated with an ASR system and explore efficiently combining these low-quality transcripts with a small amount of high quality transcripts. Standard n-gram language models are sensitive to the quality of the highest order n-gram and are unable to exploit accurate weaker statistics. Instead, a log-linear language model is introduced, which elegantly incorporates a variety of background models through MAP adaptation. This work introduces marginal class constraints which effectively capture knowledge of transcriber error and improve performance over n-gram features. Finally, this work constrains the language modeling task to keyword search of words unseen in the training text. While overall system performance is good, these words suffer the most due to a low probability in the language model. Semi-supervised learning effectively extracts likely n-grams containing these new keywords from a large corpus of audio. By using a search metric that favors recall over precision, this method captures over 80% of the potential gain

Confusion modelling for lip-reading

Lip-reading is mostly used as a means of communication by people with hearing di�fficulties. Recent work has explored the automation of this process, with the aim of building a speech recognition system entirely driven by lip movements. However, this work has so far produced poor results because of factors such as high variability of speaker features, diffi�culties in mapping from visual features to speech sounds, and high co-articulation of visual features. The motivation for the work in this thesis is inspired by previous work in dysarthric speech recognition [Morales, 2009]. Dysathric speakers have poor control over their articulators, often leading to a reduced phonemic repertoire. The premise of this thesis is that recognition of the visual speech signal is a similar problem to recog- nition of dysarthric speech, in that some information about the speech signal has been lost in both cases, and this brings about a systematic pattern of errors in the decoded output. This work attempts to exploit the systematic nature of these errors by modelling them in the framework of a weighted finite-state transducer cascade. Results indicate that the technique can achieve slightly lower error rates than the conventional approach. In addition, it explores some interesting more general questions for automated lip-reading

Phoneme segmentation and Voice activity detection

This internship was intended to be a continuation of my work last year with the same team, whose focus is non-linear methods for complex signal analysis using concepts of scale invariance and particularly the development of a new multiscale microcanonical formalism (MMF). While the fields of application of this new formalism are diverse, one of them is speech processing. My contribution was exploratory research into innovative methods for text-independent phoneme segmentation which conform to a "linear" model, the goal being to provide a performance comparison with the "non-linear" MMF-based methods under development by the other team members. This year I focused on two areas: a continuation of last year's work in phoneme segmentation, and implementation of voice activity detection algorithms. For the continuation of last year's work, I performed experiments with more rigor in order to better understand the results I obtained last year. I re-examined the algorithms I implemented last year and corrected discrepancies, and brought the implementations closer into line with standard practice. Some of the work to this end is described in a section in the Appendix A. I performed the requisite experiments to evaluate the performance of these methods on a standard database used for phoneme segmentation. I continued past this point with experiments on two other segmentation methods, in preparation for publication of a comprehensive journal paper. I made improvements to the functioning some of these methods, and in some instances I was able to improve the performance of the algorithms. In addition to phoneme segmentation, the team is interested in applying the MMF to the field of Voice Activity Detection (VAD). It was desired that I implement several so-called "classical" VAD algorithms to serve as a basis for comparison for the new, non-linear algorithms which will be developed by the team in the future. As such I implemented four VAD algorithms commonly used as references in the literature to function as a standard reference for the new methods being developed. Further, I implemented a framework for evaluation of VAD algorithms. This consisted in devising methods for generating test databases for use in evaluating the performance of VAD algorithms and implementing them in code. Also under this effort, I wrote programs for scoring the output of these algorithms. I adapted existing code for two standard VADs to function within this framework, and finally evaluated these VADs under different conditions

On Experimentation in Software-Intensive Systems

Context: Delivering software that has value to customers is a primary concern of every software company. Prevalent in web-facing companies, controlled experiments are used to validate and deliver value in incremental deployments. At the same that web-facing companies are aiming to automate and reduce the cost of each experiment iteration, embedded systems companies are starting to adopt experimentation practices and leverage their activities on the automation developments made in the online domain. Objective: This thesis has two main objectives. The first objective is to analyze how software companies can run and optimize their systems through automated experiments. This objective is investigated from the perspectives of the software architecture, the algorithms for the experiment execution and the experimentation process. The second objective is to analyze how non web-facing companies can adopt experimentation as part of their development process to validate and deliver value to their customers continuously. This objective is investigated from the perspectives of the software development process and focuses on the experimentation aspects that are distinct from web-facing companies. Method: To achieve these objectives, we conducted research in close collaboration with industry and used a combination of different empirical research methods: case studies, literature reviews, simulations, and empirical evaluations. Results: This thesis provides six main results. First, it proposes an architecture framework for automated experimentation that can be used with different types of experimental designs in both embedded systems and web-facing systems. Second, it proposes a new experimentation process to capture the details of a trustworthy experimentation process that can be used as the basis for an automated experimentation process. Third, it identifies the restrictions and pitfalls of different multi-armed bandit algorithms for automating experiments in industry. This thesis also proposes a set of guidelines to help practitioners select a technique that minimizes the occurrence of these pitfalls. Fourth, it proposes statistical models to analyze optimization algorithms that can be used in automated experimentation. Fifth, it identifies the key challenges faced by embedded systems companies when adopting controlled experimentation, and we propose a set of strategies to address these challenges. Sixth, it identifies experimentation techniques and proposes a new continuous experimentation model for mission-critical and business-to-business. Conclusion: The results presented in this thesis indicate that the trustworthiness in the experimentation process and the selection of algorithms still need to be addressed before automated experimentation can be used at scale in industry. The embedded systems industry faces challenges in adopting experimentation as part of its development process. In part, this is due to the low number of users and devices that can be used in experiments and the diversity of the required experimental designs for each new situation. This limitation increases both the complexity of the experimentation process and the number of techniques used to address this constraint

Strategies for Handling Out-of-Vocabulary Words in Automatic Speech Recognition

Nowadays, most ASR (automatic speech recognition) systems deployed in industry are closed-vocabulary systems, meaning we have a limited vocabulary of words the system can recognize, and where pronunciations are provided to the system. Words out of this vocabulary are called out-of-vocabulary (OOV) words, for which either pronunciations or both spellings and pronunciations are not known to the system. The basic motivations of developing strategies to handle OOV words are: First, in the training phase, missing or wrong pronunciations of words in training data results in poor acoustic models. Second, in the test phase, words out of the vocabulary cannot be recognized at all, and mis-recognition of OOV words may affect recognition performance of its in-vocabulary neighbors as well. Therefore, this dissertation is dedicated to exploring strategies of handling OOV words in closed-vocabulary ASR. First, we investigate dealing with OOV words in ASR training data, by introducing an acoustic-data driven pronunciation learning framework using a likelihood-reduction based criterion for selecting pronunciation candidates from multiple sources, i.e. standard grapheme-to-phoneme algorithms (G2P) and phonetic decoding, in a greedy fashion. This framework effectively expands a small hand-crafted pronunciation lexicon to cover OOV words, for which the learned pronunciations have higher quality than approaches using G2P alone or using other baseline pruning criteria. Furthermore, applying the proposed framework to generate alternative pronunciations for in-vocabulary (IV) words improves both recognition performance on relevant words and overall acoustic model performance. Second, we investigate dealing with OOV words in ASR test data, i.e. OOV detection and recovery. We first conduct a comparative study of a hybrid lexical model (HLM) approach for OOV detection, and several baseline approaches, with the conclusion that the HLM approach outperforms others in both OOV detection and first pass OOV recovery performance. Next, we introduce a grammar-decoding framework for efficient second pass OOV recovery, showing that with properly designed schemes of estimating OOV unigram probabilities, the framework significantly improves OOV recovery and overall decoding performance compared to first pass decoding. Finally we propose an open-vocabulary word-level recurrent neural network language model (RNNLM) re-scoring framework, making it possible to re-score lattices containing recovered OOVs using a single word-level RNNLM, that was ignorant of OOVs when it was trained. Above all, the whole OOV recovery pipeline shows the potential of a highly efficient open-vocabulary word-level ASR decoding framework, tightly integrated into a standard WFST decoding pipeline

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Automatic detection of disfluencies in a corpus of university lectures

This dissertation focuses on the identification of disfluent sequences and their distinct structural regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32 hours of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. The best results concern the detection of the interregnum, followed by the detection of the interruption point. Several machine learning methods have been applied, but experiments show that Classification and Regression Trees usually outperform the other methods. The set of most informative features for cross-region identification encompasses word duration ratios, word confidence score, silent ratios, and pitch and energy slopes. Features such as the number of phones and syllables per word proved to be more useful for the identification of the interregnum, whereas energy slopes were most suited for identifying the interruption point. We have also conducted initial experiments on automatic detecting filled pauses, the most frequent disfluency type. For now, only force aligned transcripts were used, since the ASR system is not well adapted to this domain. This study is a step towards automatic detection of filled pauses for European Portuguese using prosodic features. Future work will extend this study for fully automatic transcripts, and will also tackle other domains, also exploring extended sets of linguistic features.Esta tese aborda a identificação de sequências disfluentes e respetivas regiões estruturais. As experiências aqui descritas baseiam-se em segmentação e informação relativa a prosódia, calculadas a partir de um corpus de aulas universitárias em Português Europeu, contendo cerca de 32 horas de fala e de cerca de 7,7% de disfluências. O conjunto de características utilizadas provou ser discriminatório na identificação das regiões contidas na produção de disfluências. Os melhores resultados dizem respeito à deteção do interregnum, seguida da deteção do ponto de interrupção. Foram testados vários métodos de aprendizagem automática, sendo as Árvores de Decisão e Regressão as que geralmente obtiveram os melhores resultados. O conjunto de características mais informativas para a identificação e distinção de regiões disfluentes abrange rácios de duração de palavras, nível de confiança da palavra atual, rácios envolvendo silêncios e declives de pitch e de energia. Características tais como o número de fones e sílabas por palavra provaram ser mais úteis para a identificação do interregnum, enquanto pitch e energia foram os mais adequados para identificar o ponto de interrupção. Foram também realizadas experiências focando a deteção de pausas preenchidas. Por enquanto, para estas experiências foi utilizado apenas material proveniente de alinhamento forçado, já que o sistema de reconhecimento automático não está bem adaptado a este domínio. Este estudo representa um novo passo no sentido da deteção automática de pausas preenchidas para Português Europeu, utilizando recursos prosódicos. Em trabalho futuro pretende-se estender esse estudo para transcrições automáticas e também abordar outros domínios, explorando conjuntos mais extensos de características linguísticas