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    Design of vocabulary-independent Mandarin keyword spotters
    (2000-07-01)
    Liu, CM
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    Chiu, CC
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    Chang, HY
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    資訊工程學系
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    Department of Computer Science
    This paper considers the design of a vocabulary-independent keyword spotter for Mandarin speech according to the framework by Huang et al. [1]. This paper considers three varieties of filler model structures for the framework based on subsyllabic grammar of Mandarin speech. On the basis of the three structures, we infer the problems of this framework through three arguments and presents two methods to modify the spotting vehicle.
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    Incremental MLLR speaker adaptation by fuzzy logic control
    (2007-11-01)
    Ding, Ing-Jr
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    資訊工程學系
    ;
    Department of Computer Science
    This paper presents a fuzzy control mechanism for conventional maximum likelihood linear regression (MLLR) speaker adaptation, called FLC-MLLR, by which the effect of MLLR adaptation is regulated according to the availability of adaptation data in such a way that the advantage of MLLR adaptation could be fully exploited when the training data are sufficient, or the consequence of poor MLLR adaptation would be restrained otherwise. The robustness of MLLR adaptation against data scarcity is thus ensured. The proposed mechanism is conceptually simple and computationally inexpensive and effective; the experiments in recognition rate show that FLC-MLLR outperforms standard MLLR especially when encountering data insufficiency and performs better than MAPLR at much less computing cost. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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    A speech recognition method based on the sequential multi-layer perceptrons
    (1996-06-01)
    Chen, WY
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    Chen, SH
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    Lin, CJ
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    交大名義發表
    ;
    National Chiao Tung University
    A novel multi-layer perceptrons (MLP)-based speech recognition method is proposed in this study. In this method, the dynamic time warping capability of hidden Markov models (HMM) is directly combined with the discriminant based learning of MLP for the sake of employing a sequence of MLPs (SMLP) as a word recognizer. Each MLP is regarded as a state recognizer to distinguish an acoustic event. Next, the word recognizer is formed by serially cascading all state recognizers. Advantages of both HMM and MLP methods are attained in this system through training the SMLP with an algorithm which combines a dynamic programming (DP) procedure with a generalized probabilistic descent (GPD) algorithm. Additionally, two sub-syllable SMLP-based schemes are studied through application of this method toward the recognition of isolated Mandarin digits. Simulation results confirm that the performance of the methods is comparable to a well modeled continuous Gaussian mixture density HMM trained with the minimum error criterion. Not only does the SMLP require less trainable parameters than the HMM system, but the former is more convenient for analysing internal features. With the aid of internal feature selection, discarding the least useful parameters of SMLP without affecting its performance is relatively easy. Copyright (C) 1996 Elsevier Science Ltd