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Reducing Development Costs Of Large Vocabulary Speech Recognition Systems
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Book Synopsis Reducing Development Costs of Large Vocabulary Speech Recognition Systems by : Thiago Fraga Da Silva
Download or read book Reducing Development Costs of Large Vocabulary Speech Recognition Systems written by Thiago Fraga Da Silva and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the outstanding challenges in large vocabulary automatic speech recognition (ASR) is the reduction of development costs required to build a new recognition system or adapt an existing one to a new task, language or dialect. The state-of-the-art ASR systems are based on the principles of the statistical learning paradigm, using information provided by two stochastic models, an acoustic (AM) and a language (LM) model. The standard methods used to estimate the parameters of such models are founded on two main assumptions : the training data sets are large enough, and the training data match well the target task. It is well-known that a great part of system development costs is due to the construction of corpora that fulfill these requirements. In particular, manually transcribing the audio data is the most expensive and time-consuming endeavor. For some applications, such as the recognition of low resourced languages or dialects, finding and collecting data is also a hard (and expensive) task. As a means to lower the cost required for ASR system development, this thesis proposes and studies methods that aim to alleviate the need for manually transcribing audio data for a given target task. Two axes of research are explored. First, unsupervised training methods are explored in order to build three of the main components of ASR systems : the acoustic model, the multi-layer perceptron (MLP) used to extract acoustic features and the language model. The unsupervised training methods aim to estimate the model parameters using a large amount of automatically (and inaccurately) transcribed audio data, obtained thanks to an existing recognition system. A novel method for unsupervised AM training that copes well with the automatic audio transcripts is proposed : the use of multiple recognition hypotheses (rather than the best one) leads to consistent gains in performance over the standard approach. Unsupervised MLP training is proposed as an alternative to build efficient acoustic models in a fully unsupervised way. Compared to cross-lingual MLPs trained in a supervised manner, the unsupervised MLP leads to competitive performance levels even if trained on only about half of the data amount. Unsupervised LM training approaches are proposed to estimate standard back-off n-gram and neural network language models. It is shown that unsupervised LM training leads to additive gains in performance on top of unsupervised AM training. Second, this thesis proposes the use of model interpolation as a rapid and flexible way to build task specific acoustic models. In reported experiments, models obtained via interpolation outperform the baseline pooled models and equivalent maximum a posteriori (MAP) adapted models. Interpolation proves to be especially useful for low resourced dialect ASR. When only a few (2 to 3 hours) or no acoustic data truly matching the target dialect are available for AM training, model interpolation leads to substantial performance gains compared to the standard training methods.
Book Synopsis Class Reduction for Isolated Word Large Vocabulary Speech Recognition Systems by : Ashutosh Joshi
Download or read book Class Reduction for Isolated Word Large Vocabulary Speech Recognition Systems written by Ashutosh Joshi and published by . This book was released on 2002 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Progressive-Search Algorithms for Large-Vocabulary Speech Recognition by :
Download or read book Progressive-Search Algorithms for Large-Vocabulary Speech Recognition written by and published by . This book was released on 1993 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors describe a technique they call "Progressive Search," which is useful for developing and implementing speech recognition systems with high computational requirements. The scheme iteratively uses more and more complex recognition schemes, where each iteration constrains the search space of the next. An algorithm, the "Forward-Backward Word-Life Algorithm," is described. It can generate a word lattice in a progressive search that would be used as a language model embedded in a succeeding recognition pass to reduce computation requirements. They show that speed-ups of more than an order of magnitude are achievable with only minor costs in accuracy.
Book Synopsis Speaker Adaptation in a Large-vocabulary Speech Recognition System by : Dimitry Rtischev
Download or read book Speaker Adaptation in a Large-vocabulary Speech Recognition System written by Dimitry Rtischev and published by . This book was released on 1989 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of Rehabilitation Research and Development by :
Download or read book Journal of Rehabilitation Research and Development written by and published by . This book was released on 1994 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of Rehabilitation Research & Development by :
Download or read book Journal of Rehabilitation Research & Development written by and published by . This book was released on 2005 with total page 1172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book ICASSP 86 written by and published by . This book was released on 1986 with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Speech Recognition A Complete Guide - 2020 Edition by : Gerardus Blokdyk
Download or read book Speech Recognition A Complete Guide - 2020 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2020-02-20 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Is speech recognition becoming mainstream? Does your application use large vocabulary continuous speech recognition (LVCSR) technology? Are you using applications and software as AI, machine learning or natural speech recognition that make more sense to run in a cloud-based setting? How far has voice technology advanced since the early, garbled days of speech recognition software? Is voice biometrics linked to speech recognition? This powerful Speech Recognition self-assessment will make you the assured Speech Recognition domain expert by revealing just what you need to know to be fluent and ready for any Speech Recognition challenge. How do I reduce the effort in the Speech Recognition work to be done to get problems solved? How can I ensure that plans of action include every Speech Recognition task and that every Speech Recognition outcome is in place? How will I save time investigating strategic and tactical options and ensuring Speech Recognition costs are low? How can I deliver tailored Speech Recognition advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Speech Recognition essentials are covered, from every angle: the Speech Recognition self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Speech Recognition outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Speech Recognition practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Speech Recognition are maximized with professional results. Your purchase includes access details to the Speech Recognition self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Speech Recognition Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Book Synopsis Speaker Adaptation in a Large-vocabulary Speech Recognizer Via VQ Prototype Modification by : Dimitry Rtischev
Download or read book Speaker Adaptation in a Large-vocabulary Speech Recognizer Via VQ Prototype Modification written by Dimitry Rtischev and published by . This book was released on 1989 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "The problem of adapting the parameters of a speaker-dependent speech recognition system to a different speaker is examined with the objective of reducing or eliminating recognizer training necessary for user enrollment. A statistical approach to speech recognition based on vector quantization (VQ) and hidden Markov modeling (HMM) of speech is considered. The emphasis is on adaptation of vector quantizer prototypes as opposed to modification of hidden Markov model parameters. Two statistical techniques for VQ prototype adaptation, namely Bayesian learning and tied-mixture continuous-parameter HMM's, are presented and evaluated on the basis of experimental evidence. It is concluded that whereas Bayesian adaptation offers the best compromise between performance, amount of training data, and computational expense, tied-mixture continuous parameter HMM's constitute an even more reliable and effective technique for speaker adaptation."
Book Synopsis Cross-language Acoustic Adaptation for Automatic Speech Recognition by : Christoph Nieuwoudt
Download or read book Cross-language Acoustic Adaptation for Automatic Speech Recognition written by Christoph Nieuwoudt and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech recognition systems have been developed for the major languages of the world, yet for the majority of languages there are currently no large vocabulary continuous speech recognition (LVCSR) systems. The development of an LVCSR system for a new language is very costly, mainly because a large speech database has to be compiled to robustly capture the acoustic characteristics of the new language. This thesis investigates techniques that enable the re-use of acoustic information from a source language, in which a large amount of data is available, in implementing a system for a new target language. The assumption is that too little data is available in the target language to train a robust speech recognition system on that data alone, and that use of acoustic information from a source language can improve the performance of a target language recognition system. Strategies for cross-language use of acoustic information are proposed, including training on pooled source and target language data, adaptation of source language models using target language data, adapting multilingual models using target language data and transforming source language data to augment target language data for model training. These strategies are allied with Bayesian and transformation-based techniques, usually used for speaker adaptation, as well as with discriminative learning techniques, to present a framework for cross-language re-use of acoustic information. Extensions to current adaptation techniques are proposed to improve the performance of these techniques specifically for cross-language adaptation. A new technique for transformation-based adaptation of variance parameters and a cost-based extension of the minimum classification error (MCE) approach are proposed. Experiments are performed for a large number of approaches from the proposed framework for cross-language re-use of acoustic information. Relatively large amounts of English speech data are used in conjunction with smaller amounts of Afrikaans speech data to improve the performance of an Afrikaans speech recogniser. Results indicate that a significant reduction in word error rate (between 26% and 50%, depending on the amount of Afrikaans data available) is possible when English acoustic data is used in addition to Afrikaans speech data from the same database (i.e both sets of data were recorded under the same c1̀2onditions and the same labelling process was used). For same-database experiments, best results are achieved for approaches that train models on pooled source and target language data and then perform further adaptation of the models using Bayesian or discriminative techniques on target language data only. Experiments are also performed to evaluate the use of English data from a different database than the Afrikaans data. Peak reductions in word error rate of between 16% and 35% are delivered, depending on the amount of Afrikaans data available. Best results are achieved for an approach that performs a simple transformation of source model parameters using target language data, and then performs Bayesian adaptation of the transformed model on target language data.
Book Synopsis The Handbook of Computational Linguistics and Natural Language Processing by : Alexander Clark
Download or read book The Handbook of Computational Linguistics and Natural Language Processing written by Alexander Clark and published by John Wiley & Sons. This book was released on 2013-04-24 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
Book Synopsis Speech Science and Technology by : Shuzo Saito
Download or read book Speech Science and Technology written by Shuzo Saito and published by IOS Press. This book was released on 1992 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Large Vocabulary Continuous Speech Recognition System with High Predictability by : Minoru Shigenaga
Download or read book A Large Vocabulary Continuous Speech Recognition System with High Predictability written by Minoru Shigenaga and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pattern Recognition in Speech and Language Processing by : Wu Chou
Download or read book Pattern Recognition in Speech and Language Processing written by Wu Chou and published by CRC Press. This book was released on 2003-02-26 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Reco
Book Synopsis Progressive Word Hypotheses Reduction for Very Large Vocabulary, Continuous Speech Recognition by :
Download or read book Progressive Word Hypotheses Reduction for Very Large Vocabulary, Continuous Speech Recognition written by and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Progressive Word Hypotheses Reduction for Very Large Vocabulary, Continuous Speech Recognition by :
Download or read book Progressive Word Hypotheses Reduction for Very Large Vocabulary, Continuous Speech Recognition written by and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Optimization of Acoustic Models for Large Vocabulary Speech Recognition by : Rusheng Hu
Download or read book Statistical Optimization of Acoustic Models for Large Vocabulary Speech Recognition written by Rusheng Hu and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation investigates optimization of acoustic models in speech recognition. Two new optimization methods are proposed for phonetic decision tree (PDT) search and Hidden Markov modeling (HMM)-- the knowledge-based adaptive PDT algorithm and the HMM gradient boosting algorithm. Investigations are conducted to applying both methods to improve word error rate of the state-of-the-art speech recognition system. However, these two methods are developed in a general machine learning background and their applications are not limited to speech recognition. The HMM gradient boosting method is based on a function approximation scheme from the perspective of optimization in function space rather than the parameter space, based on the fact that the Gaussian mixture model in each HMM state is an additive model of homogeneous functions (Gaussians). It provides a new scheme which can jointly optimize model structure and parameters. Experiments are conducted on the World Street Journal (WSJ) task and good improvements on word error rate are observed. The knowledge-based adaptive PDT algorithm is developed under a trend toward knowledge-based systems and aims at optimizing the mapping from contextual phones to articulatory states by maximizing implicit usage of the phonological and phonetic information, which is presumed to be contained in large data corpus. A computational efficient algorithm is developed to incorporate this prior knowledge in PDT construction. This algorithm is evaluated on the Telehealth conversational speech recognition and significant improvement on system performance is achieved.
