NDLI: Selective training for hidden Markov models with applications to speech classification

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Selective training for hidden Markov models with applications to speech classification

Content Provider	IEEE Xplore Digital Library
Author	Arslan, L.M. Hansen, J.H.L.
Copyright Year	1993
Abstract	Traditional maximum likelihood estimation of hidden Markov model parameters aims at maximizing the overall probability across the training tokens of a given speech unit. As such, it disregards any interaction or biases across the models in the training procedure. Often, the resulting model parameters do not result in minimum error classification in the training set. A new selective training method is proposed that controls the influence of outliers in the training data on the generated models. The resulting models are shown to possess feature statistics which are more clearly separated for confusable patterns. The proposed selective training procedure is used for hidden Markov model training, with application to foreign accent classification, language identification, and speech recognition using the E-set alphabet. The resulting error rates are measurably improved over traditional forward-backward training under open test conditions. The proposed method is similar in terms of its goal to maximum mutual information estimation training, however it requires less computation, and the convergence properties of maximum likelihood estimation are retained in the new formulation.
Starting Page	46
Ending Page	54
Page Count	9
File Size	214781
File Format	PDF
ISSN	10636676
Volume Number	7
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Maximum likelihood estimation Training data Statistics Natural languages Speech recognition Error analysis Testing Mutual information Convergence
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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