Proceedings ArticleDOI
Lexicon-building methods for an acoustic sub-word based speech recognizer
Kuldip K. Paliwal
- Vol. 1990, pp 729-732
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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Proceedings Article
Using automatically-derived acoustic sub-word units in large vocabulary speech recognition.
Michiel Bacchiani,Mari Ostendorf +1 more
TL;DR: The joint solution to the problems of learning a unit inventory and corresponding lexicon from data is described and the methodology is extended to handle infrequently observed words using a hybrid system that combines automatically-derived units with phone-based units.
Speech recognition system design based on automatically derived units
Michiel Bacchiani,Mari Ostendorf +1 more
TL;DR: This thesis addresses previously unsolved problems in automatic unit design with three main contributions: to make design of a large unit inventory practical, a new approach is described that combines the problems of unit selection and lexicon design, and the algorithm for learning context conditioning groups is successful.
Proceedings ArticleDOI
Speech recognition based on acoustically derived segment units
TL;DR: The authors propose an ASU-based word model generation method by composing the ASU statistics, that is, their means, variances and durations, and the effectiveness of the proposed method is shown through spontaneous word recognition experiments.
Dissertation
Discovering linguistic structures in speech : models and applications
TL;DR: A class of probabilistic models that discover the latent linguistic structures of a language directly from acoustic signals are developed, and this approach contrasts sharply with the typical method of creating such a dictionary by human experts, which can be a time-consuming and expensive endeavor.
Proceedings ArticleDOI
Speech acoustic unit segmentation using hierarchical dirichlet processes.
TL;DR: This work introduces a nonparametric Bayesian approach for segmentation, based on Hierarchical Dirichlet Processes (HDP), in which a hidden Markov model (HMM) with an unbounded number of states is used to segment the utterance.
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.