Hidden Markov Models for Speech Recognition
作者:
B.H. Juang,
L.R. Rabiner,
期刊:
Technometrics
(Taylor Available online 1991)
卷期:
Volume 33,
issue 3
页码: 251-272
ISSN:0040-1706
年代: 1991
DOI:10.1080/00401706.1991.10484833
出版商: Taylor & Francis Group
关键词: Baum–Welch algorithm;Incomplete data problem;Maximum a posteriori decoding;Maximum likelihood
数据来源: Taylor
摘要:
The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework; the ease and availability of training algorithms for cstimating the parameters of the models from finite training sets of speech data; the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds, and so forth; and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations.
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