Automatic language identification using Gaussian mixture and hidden Markov models
M.A. Zissman
- 27 Apr 1993
- Vol. 2, pp 399-402
TL;DR: In general, the performance of a single state HMM was comparable with that of the multistate HMMs, indicating that the sequential modeling capabilities of HMMs were not exploited.
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Abstract: Ergodic, continuous-observation, hidden Markov models (HMMs) were used to perform automatic language classification and detection of speech messages. State observation probability densities were modeled as tied Gaussian mixtures. The algorithm was evaluated on four multilanguage speech databases: a three language subset of the Spoken Language Library, a three language subset of a five-language Rome Laboratory database, the 20-language CCITT database, and the ten-language OGI (Oregon Graduate Institute) telephone speech database. In general, the performance of a single state HMM (i.e., a static Gaussian mixture classifier) was comparable with that of the multistate HMMs, indicating that the sequential modeling capabilities of HMMs were not exploited. >
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Comparison of four approaches to automatic language identification of telephone speech
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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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Masahide Sugiyama
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