12 research outputs found

    On the Adequacy of Baseform Pronunciations and Pronunciation Variants

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    This paper presents an approach to automatically extract and evaluate the ``stability'' of pronunciation variants (i.e., adequacy of the model to accommodate this variability), based on multiple pronunciations of each lexicon words and the knowledge of a reference baseform pronunciation. Most approaches toward modelling pronunciation variability in speech recognition are based on the inference (through an ergodic HMM model) of a pronunciation graph (including all pronunciation variants), usually followed by a smoothing (e.g., Bayesian) of the resulting graph. Compared to these approaches, the approach presented here differs by (1) the way the models are inferred and (2) the way the smoothing (i.e., keeping the best ones) is done. In our case, indeed, inference of the pronunciation variants is obtained by slowly ``relaxing'' a (usually left-to-right) baseform model towards a fully ergodic model. In this case, the more stable the model is, the less the inferred model will diverge from it. Hence, for each pronunciation model so generated, we evaluate their adequacy by calculating the Levenshtein distance of the the new model with respect to the baseform, as well as their confidence measure (based on some posterior estimation), and models with the lowest Levenshtein distance and highest confidence are preserved. On a large telephone speech database (Phonebook), we show the relationship between this ``stability'' measure and recognition performance, and we finally show that automatically adding a few pronunciation variants to the less stable words is enough to significantly improve recognition rates

    Using auxiliary sources of knowledge for automatic speech recognition

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    Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variation, speaker variation. This leads to different kinds of mismatch, such as, mismatch between acoustic features and acoustic models or mismatch between acoustic features and pronunciation models (given the acoustic models). The main focus of this work is on integrating auxiliary knowledge sources into standard ASR systems so as to make the acoustic models more robust to the variabilities in the speech signal. We refer to the sources of knowledge that are able to provide additional information about the sources of variability as auxiliary sources of knowledge. The auxiliary knowledge sources that have been primarily investigated in the present work are auxiliary features and auxiliary subword units. Auxiliary features are secondary source of information that are outside of the standard cepstral features. They can be estimation from the speech signal (e.g., pitch frequency, short-term energy and rate-of-speech), or additional measurements (e.g., articulator positions or visual information). They are correlated to the standard acoustic features, and thus can aid in estimating better acoustic models, which would be more robust to variabilities present in the speech signal. The auxiliary features that have been investigated are pitch frequency, short-term energy and rate-of-speech. These features can be modelled in standard ASR either by concatenating them to the standard acoustic feature vectors or by using them to condition the emission distribution (as done in gender-based acoustic modelling). We have studied these two approaches within the framework of hybrid HMM/artificial neural networks based ASR, dynamic Bayesian network based ASR and TANDEM system on different ASR tasks. Our studies show that by modelling auxiliary features along with standard acoustic features the performance of the ASR system can be improved in both clean and noisy conditions. We have also proposed an approach to evaluate the adequacy of the baseform pronunciation model of words. This approach allows us to compare between different acoustic models as well as to extract pronunciation variants. Through the proposed approach to evaluate baseform pronunciation model, we show that the matching and discriminative properties of single baseform pronunciation can be improved by integrating auxiliary knowledge sources in standard ASR. Standard ASR systems use usually phonemes as the subword units in a Markov chain to model words. In the present thesis, we also study a system where word models are described by two parallel chains of subword units: one for phonemes and the other are for graphemes (phoneme-grapheme based ASR). Models for both types of subword units are jointly learned using maximum likelihood training. During recognition, decoding is performed using either or both of the subword unit chains. In doing so, we thus have used graphemes as auxiliary subword units. The main advantage of using graphemes is that the word models can be defined easily using the orthographic transcription, thus being relatively noise free as compared to word models based upon phoneme units. At the same time, there are drawbacks to using graphemes as subword units, since there is a weak correspondence between the grapheme and the phoneme in languages such as English. Experimental studies conducted for American English on different ASR tasks have shown that the proposed phoneme-grapheme based ASR system can perform better than the standard ASR system that uses only phonemes as its subword units. Furthermore, while modelling context-dependent graphemes (similar to context-dependent phonemes), we observed that context-dependent graphemes behave like phonemes. ASR studies conducted on different tasks showed that by modelling context-dependent graphemes only (without any phonetic information) performance competitive to the state-of-the-art context-dependent phoneme-based ASR system can be obtained

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

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    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

    Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

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    This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists

    Classical Arabic verb inflection: a WP-grammar, with an introductory phonemic investigation

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    This work presents a new grammar of the Classical Arabic Verb Inflection, carried out within the system of the WP morphological theory (the Word and Paradigm model of analysis as formalized by Professor P. H. Matthews). It is thus basically an application of this structural theory, rather than an assessment of its merits. Yet a general evaluation of characteristics of this theory, compared with two other interrelated systems, is presented with"particular attention to the concept of adequacy' in relation to Arabic grammar. The thesis consists of six chapters, the first of which represents an elaborated introduction meant to define the implicit questionable points that the title may raise. This is followed by a chapter on phonemic investigation, restricted to the problematic areas where the scholarly dispute over a specific number of Arabic phonemes has been building up since the Classical era. The terminological distinctions between the basic traditional terms of Arabic grammar and their presumed equivalents in modern linguistics is discussed in Chapter III as a prelude to the major body of the work. Chapter IV reviews, first, the three relevant linguistic models of analysis in relation to the morphology of Classical Arabic, which is taken here beyond the restrictive study of the individual language to the domain of the general linguistic theory; and, second, it presents a comprehensive summary of WP: its basic terms, rule system and evaluational procedure, followed by the reasons that made it the ideal choice for the present purpose. Chapter V, which serves as a background to the application in Chapter VI, represents the core of the discussions devoted to the Classical Arabic verbal system. It comprises all the explanations that are possibly needed for the making and understanding of the grammatical rules, and which find no room in the final chapter without interrupting the flow of the rule divisions. The final chapter is merely an application of the WP model to the inflectional system of the Classical Arabic verb. It consists of the verbal grammatical rules, preceded by a minimized group of the required guiding notes, and followed by an exemplary demonstration of the drivational system. The thesis is ended with a Summary and Conclusions that survey the work in general and briefly record its findings. In addition to the original views and postulations distributed over almost all the chapters of this work, and apart from the empirical value regarding the theory adopted, the present grammar represents on the one hand a further step in the evolutional course of the Classical Arabic grammar, and on the other it provides a new link between this classical grammar and the continual evolution of the linguistic theory

    Dysarthric speech analysis and automatic recognition using phase based representations

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    Dysarthria is a neurological speech impairment which usually results in the loss of motor speech control due to muscular atrophy and poor coordination of articulators. Dysarthric speech is more difficult to model with machine learning algorithms, due to inconsistencies in the acoustic signal and to limited amounts of training data. This study reports a new approach for the analysis and representation of dysarthric speech, and applies it to improve ASR performance. The Zeros of Z-Transform (ZZT) are investigated for dysarthric vowel segments. It shows evidence of a phase-based acoustic phenomenon that is responsible for the way the distribution of zero patterns relate to speech intelligibility. It is investigated whether such phase-based artefacts can be systematically exploited to understand their association with intelligibility. A metric based on the phase slope deviation (PSD) is introduced that are observed in the unwrapped phase spectrum of dysarthric vowel segments. The metric compares the differences between the slopes of dysarthric vowels and typical vowels. The PSD shows a strong and nearly linear correspondence with the intelligibility of the speaker, and it is shown to hold for two separate databases of dysarthric speakers. A systematic procedure for correcting the underlying phase deviations results in a significant improvement in ASR performance for speakers with severe and moderate dysarthria. In addition, information encoded in the phase component of the Fourier transform of dysarthric speech is exploited in the group delay spectrum. Its properties are found to represent disordered speech more effectively than the magnitude spectrum. Dysarthric ASR performance was significantly improved using phase-based cepstral features in comparison to the conventional MFCCs. A combined approach utilising the benefits of PSD corrections and phase-based features was found to surpass all the previous performance on the UASPEECH database of dysarthric speech

    Phonology in the Twentieth Century

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    The original (1985) edition of this work attempted to cover the main lines of development of phonological theory from the end of the 19th century through the early 1980s. Much work of importance, both theoretical and historiographic, has appeared in subsequent years, and the present edition tries to bring the story up to the end of the 20th century, as the title promised. This has involved an overall editing of the text, in the process correcting some errors of fact and interpretation, as well as the addition of new material and many new references

    Word Knowledge and Word Usage

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    Word storage and processing define a multi-factorial domain of scientific inquiry whose thorough investigation goes well beyond the boundaries of traditional disciplinary taxonomies, to require synergic integration of a wide range of methods, techniques and empirical and experimental findings. The present book intends to approach a few central issues concerning the organization, structure and functioning of the Mental Lexicon, by asking domain experts to look at common, central topics from complementary standpoints, and discuss the advantages of developing converging perspectives. The book will explore the connections between computational and algorithmic models of the mental lexicon, word frequency distributions and information theoretical measures of word families, statistical correlations across psycho-linguistic and cognitive evidence, principles of machine learning and integrative brain models of word storage and processing. Main goal of the book will be to map out the landscape of future research in this area, to foster the development of interdisciplinary curricula and help single-domain specialists understand and address issues and questions as they are raised in other disciplines
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