Proceedings ArticleDOI
Lexicon-building methods for an acoustic sub-word based speech recognizer
Kuldip K. Paliwal
- Vol. 1990, pp 729-732
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TLDR
The use of an acoustic subword unit (ASWU)-based speech recognition system for the recognition of isolated words is discussed and it is shown that the use of a modified k-means algorithm on the likelihoods derived through the Viterbi algorithm provides the best deterministic-type of word lexicon.Abstract:
The use of an acoustic subword unit (ASWU)-based speech recognition system for the recognition of isolated words is discussed. Some methods are proposed for generating the deterministic and the statistical types of word lexicon. It is shown that the use of a modified k-means algorithm on the likelihoods derived through the Viterbi algorithm provides the best deterministic-type of word lexicon. However, the ASWU-based speech recognizer leads to better performance with the statistical type of word lexicon than with the deterministic type. Improving the design of the word lexicon makes it possible to narrow the gap in the recognition performances of the whole word unit (WWU)-based and the ASWU-based speech recognizers considerably. Further improvements are expected by designing the word lexicon better. >read more
Citations
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Voice query-by-example for resource-limited languages using an ergodic hidden Markov model of speech
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Cluster Size Management in Multi-Stage Agglomerative Hierarchical Clustering of Acoustic Speech Segments.
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TL;DR: This work proposes the integration of a simple space management strategy into the iterative process, and shows experimentally that this leads to no loss in performance in terms of F-measure while guaranteeing that a threshold space complexity is not breached.
Proceedings ArticleDOI
A nonparametric Bayesian approach for automatic discovery of a lexicon and acoustic units
TL;DR: It is demonstrated that a speech recognition system using these discovered resources can approach the performance of a speech recognizer trained using resources developed by experts.
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Optimization of units for continuous-digit recognition task.
TL;DR: In this work, new constraints on the units were introduced: 1) they should contain suÆcient statistics of the features and 2) they must contain su ÆcientStatistics of the vocabulary.
Dissertation
Automatic determination of sub-word units for automatic speech recognition
TL;DR: This thesis presents a method for the automatic derivation of a sub-word unit inventory, whose main components are an ergodic hidden Markov model whose complexity is controlled using the Bayesian Information Criterion and an automatic generation of probabilistic dictionaries using joint multigrams.
References
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A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
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A Maximum Likelihood Approach to Continuous Speech Recognition
TL;DR: This paper describes a number of statistical models for use in speech recognition, with special attention to determining the parameters for such models from sparse data, and describes two decoding methods appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks.
Proceedings ArticleDOI
Acoustic Markov models used in the Tangora speech recognition system
TL;DR: An automatic technique for constructing Markov word models is described and results are included of experiments with speaker-dependent and speaker-independent models on several isolated-word recognition tasks.
Journal ArticleDOI
A modified K-means clustering algorithm for use in isolated work recognition
TL;DR: A clustering algorithm based on a standard K-means approach which requires no user parameter specification is presented and experimental data show that this new algorithm performs as well or better than the previously used clustering techniques when tested as part of a speaker-independent isolated word recognition system.