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Discriminative And Articulatory Feature Based Pronunciation Models For Conversational Speech Recognition
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Book Synopsis Discriminative and Articulatory Feature-based Pronunciation Models for Conversational Speech Recognition by : Preethi Jyothi
Download or read book Discriminative and Articulatory Feature-based Pronunciation Models for Conversational Speech Recognition written by Preethi Jyothi and published by . This book was released on 2013 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Finally, we show that the prior DBN model can also be improved significantly by incorporating contextual information. Such pronunciation models with context are also amenable to being incorporated into a WFST-based ASR system using our new discriminative techniques.
Book Synopsis A Syllable, Articulatory-feature, and Stress-accent Model of Speech Recognition by : Shuangyu Chang
Download or read book A Syllable, Articulatory-feature, and Stress-accent Model of Speech Recognition written by Shuangyu Chang and published by . This book was released on 2002 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Discriminative Learning for Speech Recognition by : Xiadong He
Download or read book Discriminative Learning for Speech Recognition written by Xiadong He and published by Morgan & Claypool Publishers. This book was released on 2008-08-08 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum–Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice. Table of Contents: Introduction and Background / Statistical Speech Recognition: A Tutorial / Discriminative Learning: A Unified Objective Function / Discriminative Learning Algorithm for Exponential-Family Distributions / Discriminative Learning Algorithm for Hidden Markov Model / Practical Implementation of Discriminative Learning / Selected Experimental Results / Epilogue / Major Symbols Used in the Book and Their Descriptions / Mathematical Notation / Bibliography
Book Synopsis Feature-based Articulatory-modeling Approach to Large Vocabulary Continuous Speech Recognition by : Xing Jing
Download or read book Feature-based Articulatory-modeling Approach to Large Vocabulary Continuous Speech Recognition written by Xing Jing and published by . This book was released on 2001 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Feature-based Pronunciation Modeling for Automatic Speech Recognition by : Karen Livescu
Download or read book Feature-based Pronunciation Modeling for Automatic Speech Recognition written by Karen Livescu and published by . This book was released on 2005 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) The DBN framework allows us to naturally represent the factorization of the state space of feature combinations into feature-specific factors, as well as providing standard algorithms for inference and parameter learning. We investigate the behavior of such a model in isolation using manually transcribed words. Compared to a phone-based baseline, the feature-based model has both higher coverage of observed pronunciations and higher recognition rate for isolated words. We also discuss the ways in which such a model can be incorporated into various types of end-to-end speech recognizers and present several examples of implemented systems, for both acoustic speech recognition and lipreading tasks.
Book Synopsis FSM-Based Pronunciation Modeling Using Articulatory Phonological Code by : Chi Hu
Download or read book FSM-Based Pronunciation Modeling Using Articulatory Phonological Code written by Chi Hu and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: According to articulatory phonology, the gestural score is an invariant speech representation. Though the timing schemes, i.e., the onsets and offsets, of the gestural activations may vary, the ensemble of these activations tends to remain unchanged, informing the speech content. "Gestural pattern vector" (GPV) has been proposed to encode the instantaneous gestural activations that exist across all tract variables at each time. Therefore, a gestural score with a particular timing scheme can be approximated using a GPV sequence. In this work, we propose a pronunciation modeling method that uses a finite state machine (FSM) to represent the invariance of a gestural score. Given the "canonical" gestural score of a word with a known activation timing scheme, the plausible activation onsets and offsets are recursively generated and encoded as a weighted FSM. An empirical measure is used to prune out gestural activation timing schemes that deviate too much from the "canonical" gestural score. Speech recognition is achieved by matching the recovered gestural activations to the FSM-encoded gestural scores of different speech contents. In particular, the observation distribution of each GPV is modeled by an artificial neural network and Gaussian mixture tandem model. These models are used together with the FSM-based pronunciation models in a Bayesian framework. We carry out pilot word classification experiments using synthesized data from one speaker. The proposed pronunciation modeling achieves over 90% accuracy for a vocabulary of 139 words with no training observations, outperforming direct use of the "canonical" gestural score.
Book Synopsis Text, Speech, and Dialogue by : Kamil Ekštein
Download or read book Text, Speech, and Dialogue written by Kamil Ekštein and published by Springer. This book was released on 2017-08-21 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 20th International Conference on Text, Speech, and Dialogue, TSD 2017, held in Prague, CzechRepublic, in August 2017. The 56 regular papers presented together with 3 abstracts of keynote talks were carefully reviewed and selected from 117 submissions. They focus on topics such as corpora and language resources; speech recognition; tagging, classification and parsing of text and speech; speech and spoken language generation; semantic processing of text and speech; integrating applications of text and speech processing; automatic dialogue systems; as well as multimodal techniques and modelling.
Download or read book Dynamic Speech Models written by Li Deng and published by Springer Nature. This book was released on 2022-05-31 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech dynamics refer to the temporal characteristics in all stages of the human speech communication process. This speech “chain” starts with the formation of a linguistic message in a speaker's brain and ends with the arrival of the message in a listener's brain. Given the intricacy of the dynamic speech process and its fundamental importance in human communication, this monograph is intended to provide a comprehensive material on mathematical models of speech dynamics and to address the following issues: How do we make sense of the complex speech process in terms of its functional role of speech communication? How do we quantify the special role of speech timing? How do the dynamics relate to the variability of speech that has often been said to seriously hamper automatic speech recognition? How do we put the dynamic process of speech into a quantitative form to enable detailed analyses? And finally, how can we incorporate the knowledge of speech dynamics into computerized speech analysis and recognition algorithms? The answers to all these questions require building and applying computational models for the dynamic speech process. What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain. Underlying the robust encoding and transmission of the linguistic messages are the speech dynamics at all the four levels. Mathematical modeling of speech dynamics provides an effective tool in the scientific methods of studying the speech chain. Such scientific studies help understand why humans speak as they do and how humans exploit redundancy and variability by way of multitiered dynamic processes to enhance the efficiency and effectiveness of human speech communication. Second, advancement of human language technology, especially that in automatic recognition of natural-style human speech is also expected to benefit from comprehensive computational modeling of speech dynamics. The limitations of current speech recognition technology are serious and are well known. A commonly acknowledged and frequently discussed weakness of the statistical model underlying current speech recognition technology is the lack of adequate dynamic modeling schemes to provide correlation structure across the temporal speech observation sequence. Unfortunately, due to a variety of reasons, the majority of current research activities in this area favor only incremental modifications and improvements to the existing HMM-based state-of-the-art. For example, while the dynamic and correlation modeling is known to be an important topic, most of the systems nevertheless employ only an ultra-weak form of speech dynamics; e.g., differential or delta parameters. Strong-form dynamic speech modeling, which is the focus of this monograph, may serve as an ultimate solution to this problem. After the introduction chapter, the main body of this monograph consists of four chapters. They cover various aspects of theory, algorithms, and applications of dynamic speech models, and provide a comprehensive survey of the research work in this area spanning over past 20~years. This monograph is intended as advanced materials of speech and signal processing for graudate-level teaching, for professionals and engineering practioners, as well as for seasoned researchers and engineers specialized in speech processing
Book Synopsis An Independent Assessment of Phonetic Distinctive Feature Sets Used to Model Pronunciation Variation by : Leanne Rolston
Download or read book An Independent Assessment of Phonetic Distinctive Feature Sets Used to Model Pronunciation Variation written by Leanne Rolston and published by . This book was released on 2014 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been consistently shown that Automatic Speech Recognition (ASR) performance on casual, spontaneous speech is much worse than on carefully planned or read speech by as much as double the word error rate, and that variation in pronunciation is the main reason for this degradation of performance. Thus far, any attempts to mitigate this have fallen well below expectations. Phonetic Distinctive Features show promise from a theoretical standpoint, but have thus far not been fully incorporated into an end-to-end ASR system. Work incorporating distinctive features into ASR is widespread and varied, and each project uses a unique set of features based on the authors' linguistic intuitions, so the results of these experiments cannot be fully and fairly compared. In this work, I attempt to determine which style of distinctive feature set is best suited to model pronunciation variation in ASR based on measures of surface phone prediction accuracy and efficiency of the decision tree model. Using a non-exhaustive, representative set of phonetic distinctive feature sets, decision trees were trained, one per canonical base form phone, under two experimental conditions: words in isolation, and words in sequence. These models were tested against a comparable held-out test set, and an additional data set of canonical pronunciations used to simulate formal speech. It was found that a multi-valued articulatory-based feature set provided a far more compact model that yielded comparable accuracy results, while in a comparison of binary feature sets, the model with feature redundancy provided a far more robust model, with slightly higher accuracy and, where it predicted an incorrect phone, it was closer to the actual gold standard phone than the other feature sets' predictions.
Book Synopsis Hidden Conditional Random Fields for Speech Recognition by : Yun-Hsuan Sung
Download or read book Hidden Conditional Random Fields for Speech Recognition written by Yun-Hsuan Sung and published by Stanford University. This book was released on 2010 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates using a new graphical model, hidden conditional random fields (HCRFs), for speech recognition. Conditional random fields (CRFs) are discriminative sequence models that have been successfully applied to several tasks in text processing, such as named entity recognition. Recently, there has been increasing interest in applying CRFs to speech recognition due to the similarity between speech and text processing. HCRFs are CRFs augmented with hidden variables that are capable of representing the dynamic changes and variations in speech signals. HCRFs also have the ability to incorporate correlated features from both speech signals and text without making strong independence assumptions among them. This thesis presents my current research on applying HCRFs to speech recognition and HCRFs' potential to replace the current hidden Markov model (HMM) for acoustic modeling. Experimental results of phone classification, phone recognition, and speaker adaptation are presented and discussed. Our monophone HCRFs outperform both maximum mutual information estimation (MMIE) and minimum phone error (MPE) trained HMMs and achieve the-start-of-the-art performance in TIMIT phone classification and recognition tasks. We also show how to jointly train acoustic models and language models in HCRFs, which shows improvement in the results. Maximum a posterior (MAP) and maximum conditional likelihood linear regression (MCLLR) successfully adapt speaker-independent models to speaker-dependent models with a small amount of adaptation data for HCRF speaker adaptation. Finally, we explore adding gender and dialect features for phone recognition, and experimental results are presented.
Book Synopsis Pronunciation Models by : Adam Brown
Download or read book Pronunciation Models written by Adam Brown and published by NUS Press. This book was released on 1991 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most books on pronunciation teaching deal extensively with methodology, while giving insufficient attention to the prior questions of the model being used. This book discusses the what rather than the how. It examines critically the kinds of pronunciation model in use in ELT, in particular the Received Pronunciation accent, and shows that they are unsatisfactory in several respects. Various criteria for models are investigated, especially the concepts of intelligibility, identity, and functional load. The importance of features of the phonological system of English is assessed against these criteria, so that priorities are established for pronunciation models. This book is important reading for English language teachers, applied linguists, ELT textbook writers, language planners, speech therapists, and anyone involved in the instruction of the spoken form of English.
Book Synopsis Statistical Pronunciation Modeling for Non-Native Speech Processing by : Rainer E. Gruhn
Download or read book Statistical Pronunciation Modeling for Non-Native Speech Processing written by Rainer E. Gruhn and published by Springer Science & Business Media. This book was released on 2011-05-08 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, the authors present a fully statistical approach to model non--native speakers' pronunciation. Second-language speakers pronounce words in multiple different ways compared to the native speakers. Those deviations, may it be phoneme substitutions, deletions or insertions, can be modelled automatically with the new method presented here. The methods is based on a discrete hidden Markov model as a word pronunciation model, initialized on a standard pronunciation dictionary. The implementation and functionality of the methodology has been proven and verified with a test set of non-native English in the regarding accent. The book is written for researchers with a professional interest in phonetics and automatic speech and speaker recognition.
Book Synopsis Pronunciation Modeling for Large Vocabulary Speech Recognition by : Arthur Kantor
Download or read book Pronunciation Modeling for Large Vocabulary Speech Recognition written by Arthur Kantor and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The large pronunciation variability of words in conversational speech is one of the major causes of low accuracy in automatic speech recognition (ASR). Many pronunciation modeling approaches have been developed to address this problem. Some explicitly manipulate the pronunciation dictionary as well as the set of the units used to define the pronunciations of words. Other approaches model the pronunciation implicitly by using long duration acoustical context to more accurately classify the spoken pronunciation unit. This thesis is a study of the relative ability of the acoustic and the pronunciation models to capture pronunciation variability in a nearly state of the art conversational telephone speech recognizer. Several methods are tested, each designed to improve the modeling accuracy of the recognizer. Some of the experiments result in a lower word error rate, but many do not, apparently because, in different ways, the accuracy gained by one part of the recognizer comes at the expense of accuracy lost or transferred from another part of the recognizer. Pronunciation variability is modeled with two approaches: from above with explicit pronunciation modeling and from below with implicit pronunciation modeling within the acoustic model. Both approaches make use of long duration context, explicitly by considering long-duration pronunciation units and implicitly by having the acoustic model consider long-duration speech segments. Some pronunciation models address the pronunciation variability problem by introducing multiple pronunciations per word to cover more variants observed in conversational speech. However, this can potentially increase the confusability between words. This thesis studies the relationship between pronunciation perplexity and the lexical ambiguity, which has informed the design of the explicit pronunciation models presented here.
Book Synopsis Robust Adaptation to Non-Native Accents in Automatic Speech Recognition by : Silke Goronzy
Download or read book Robust Adaptation to Non-Native Accents in Automatic Speech Recognition written by Silke Goronzy and published by Springer. This book was released on 2003-07-01 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
Book Synopsis Articulatory Features for Robust Visual Speech Recognition by : Ekaterina Saenko
Download or read book Articulatory Features for Robust Visual Speech Recognition written by Ekaterina Saenko and published by . This book was released on 2004 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores a novel approach to visual speech modeling. Visual speech, or a sequence of images of the speaker's face, is traditionally viewed as a single stream of contiguous units, each corresponding to a phonetic segment. These units are defined heuristically by mapping several visually similar phonemes to one visual phoneme, sometimes referred to as a viseme. However, experimental evidence shows that phonetic models trained from visual data are not synchronous in time with acoustic phonetic models, indicating that visemes may not be the most natural building blocks of visual speech. Instead, we propose to model the visual signal in terms of the underlying articulatory features. This approach is a natural extension of feature-based modeling of acoustic speech, which has been shown to increase robustness of audio-based speech recognition systems. We start by exploring ways of defining visual articulatory features: first in a data-driven manner, using a large, multi-speaker visual speech corpus, and then in a knowledge-driven manner, using the rules of speech production. Based on these studies, we propose a set of articulatory features, and describe a computational framework for feature-based visual speech recognition. Multiple feature streams are detected in the input image sequence using Support Vector Machines, and then incorporated in a Dynamic Bayesian Network to obtain the final word hypothesis. Preliminary experiments show that our approach increases viseme classification rates in visually noisy conditions, and improves visual word recognition through feature-based context modeling.
Book Synopsis Discriminative Feature Modeling for Statistical Speech Recognition by : Zoltán Tüske
Download or read book Discriminative Feature Modeling for Statistical Speech Recognition written by Zoltán Tüske and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :M. Margaret Withgott Publisher :Center for the Study of Language (CSLI) ISBN 13 :9780937073988 Total Pages :168 pages Book Rating :4.0/5 (739 download)
Book Synopsis Computational Models of American Speech by : M. Margaret Withgott
Download or read book Computational Models of American Speech written by M. Margaret Withgott and published by Center for the Study of Language (CSLI). This book was released on 1993 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new perspective on phonetic variation is achieved in this volume through the construction of a series of models of spoken American English. In the past, computer theorists and programmers investigating pronunciation have often relied on their own knowledge of the language or on limited transcription data. Speech recognition researchers, on the other hand, have drawn on a great deal of data but without examining in detail the information about pronunciation the data contains. The authors combine the best of each approach to develop probabilistic and rule-based computational models of transcription data. An ongoing controversy in studies of phonetic variation is the existence and proper definition of a phonetic unit. The authors argue that assumptions about the units of spoken language are critical to a computational model. Their computational models employ suprasegmental elements such as syllable boundaries, stress, and position in a unit called a metrical foot. The use of such elements in modeling data enables the creation of better computational models for both recognition and synthesis technology. This book should be of interest to speech engineers, linguists, and anyone who wishes to understand symbolic systems of communication.
