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  4. Continuous hidden markov models integrating transitional and instantaneous features for mandarin syllable recognition
 
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Continuous hidden markov models integrating transitional and instantaneous features for mandarin syllable recognition

Journal
Computer Speech and Language
Journal Volume
7
Journal Issue
3
Pages
247-263
Date Issued
1993
Author(s)
Lee, Y.
LIN-SHAN LEE  
DOI
10.1006/csla.1993.1013
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/498707
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0027625515&doi=10.1006%2fcsla.1993.1013&partnerID=40&md5=01a39109a2c7d81d5bee2a295e6d6f7c
Abstract
Feature parameters describing spectral transitions of speech signals have been properly integrated with the instantaneous features in many different approaches proposed for speech recognition, and significant performance improvements have been attained. Most of these methods are designed for recognition systems based on dynamic time warping (DTW) or discrete hidden Markov models (HMM). However, it has been experimentally shown that for the difficult problem of recognizing the highly confusing Mandarin syllables with limited amount of training data, the performances of DTW and discrete HMM techniques are much worse than that of continuous HMMs. In this paper, the performance of continuous HMMs using one type of transitional features in speaker-dependent recognition of the highly confusing Mandarin syllables is first evaluated and discussed in detail under the constraint of very limited training data. Three approaches are then proposed to integrate the instantaneous and transitional features for recognition systems based on continuous hidden Markov models. They are the most straightforward concatenation-integration approach in which the instantaneous and transitional feature vectors are simply concatenated, the two-maximization approach in which the output distribution functions for the instantaneous and transitional feature vectors are maximized separately, and the two-model approach in which two HMMs respectively for instantaneous and transitional feature vectors are independently trained but the log likelihoods are summed up with proper weighting. After extensive experiments and careful analysis, it is found that the three approaches respectively provide attractive performance under different conditions. For example, with the two-maximization approach a recognition rate (93·89%) only slightly lower than the highest achievable rate for the concatenation-integration approach (94·36% for M = 5) can be obtained at a much smaller number of mixtures (M = 2). © 1993 Academic Press. All rights reserved.
Type
journal article

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