NDLI: Predicting the Viterbi Score Distribution for a Hidden Markov Model and Application to Speech Recognition

Content Provider	IEEE Xplore Digital Library
Author	Talwar, G. Kubichek, R.F.
Copyright Year	2006
Description	Author affiliation: Electr. & Comput. Eng. Dept., Wyoming Univ., Laramie, WY (Talwar, G.; Kubichek, R.F.)
Abstract	Hidden Markov models are used in many important applications such as speech recognition and handwriting recognition. Finding an effective HMM to fit the data is important for successful operation. Typically, the Baum-Welch algorithm is used to train an HMM, and seeks to maximize the likelihood probability of the model, which is closely related to the Viterbi score. However, random initialization causes the final model quality to vary due to locally optimum solutions. Conventionally, in speech recognition systems, models are selected from a collection of already trained models using some performance criterion. In this paper, we investigate an alternative method of selecting models using the Viterbi score distribution. A method to determine the Viterbi score distribution is described based on Viterbi score variation with respect to the number of states (N) in the model. Our tests show that the distribution is approximately Gaussian when the number of states is greater than 3. The paper also investigates the relationship between performance and the Viterbi score's percentile value and discusses several interesting implications for Baum-Welch training
Starting Page	227
Ending Page	232
File Size	353021
Page Count	6
File Format	PDF
ISBN	1424435343
DOI	10.1109/DSPWS.2006.265412
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Viterbi algorithm Hidden Markov models Speech recognition Testing Predictive models Automatic speech recognition Cost function Application software Distributed computing Probability score distribution Viterbi score HMM model quality Viterbi
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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