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Title: Maximum Likelihood Successive State Splitting Algorithm for Tied-Mixture HMnet
Authors: Alexandre Girardi
Harald Singer
Kiyohiro Shikano
Satoshi Nakamura
Keywords: speech recognition
acoustic modeling
Issue Date: Oct-2000
Publisher: 電子情報通信学会
Journal Title: IEICE Transactions on Information and Systems
Volume: E83-D
Issue: 10
Start page: 1890
End page: 1897
Abstract: This paper shows how a divisive state clustering algorithm that generates acoustic Hidden Markov models(HMM)can benefit from a tied-mixture representation of the probability density function(pdf)of a state and increase the recognition performance. Popular decision tree based clustering algorthms, like for example the Successive State Splitting algorithm(SSS)make use of a simplification when clustering data. They represent a state using a single Gaussian pdf. We show that this approximation of the true pdf by a single Gaussian is too coarse, for example a single Gaussian cannot represent the differences in the symmetric parts of the pdf's of the new hypothetical states generated when evaluating the state split gain(which will determine the state split). The use of more sophisticated representations would lead to intractable computational problems that we solve by using a tied-mixture pdf representation. Additionally, we constrain the codebook to be immutable during the split. Between state spilits, this constraint is relaxed and the codebook is updated. In this paper, we thus propose an extension to the SSS algorithm, the so-called Tied-mixture Successive State Splitting algorithm(TM-SSS). TM-SSS shows up to about 31% error reduction in comparison with Maximum-Likelihood Successive State Split algorithm(ML-SSS)for a word recognition experiment.
ISSN: 0916-8532
Rights: Copyright (C) 2000 電子情報通信学会.
Text Version: publisher
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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