In this paper, a new similarity measure between HMM models which extended the well-known Kullback–Leibler distance was proposed. The Kullback–Leibler distance was defined as the mean of log-likelihood ratio (LLR) in a hypotheses test and the Kullback–Leibler distance was frequently used as a similarity measure for HMM models. Here, the standard deviation of LLR between HMM models was deviated first. Besides, the ratio of mean and standard variation of LLR was used as a new similarity measure between HMM models. Experiments were done in a Mandarin speech database, TCC-300, in order to check the effectiveness of the proposed similarity measure. The accuracy of the standard deviation of LLR estimated from the syllable HMM models was checked by comparison with the standard deviation of LLR of top-10 candidates found from HMM decoder. And, the confusion sets of 411 syllables were also found by using both the KL distance and the proposed similarity measure. Comparing to the top-10 confusion models, 94.9% and 95.3% inclusion rates can be achieved by using KL distance and the proposed similarity measure of HMM models. Index Terms — similarity measure, Kullback–Leibler distance, Hidden Markov Model
Cite as: Wang, Y.-R. (2008) A New Similarity Measure Between HMMs. Proc. International Symposium on Chinese Spoken Language Processing, 221-224
@inproceedings{wang08b_iscslp,
author={Yih-Ru Wang},
title={{A New Similarity Measure Between HMMs}},
year=2008,
booktitle={Proc. International Symposium on Chinese Spoken Language Processing},
pages={221--224}
}