KL-HMM and Probabilistic Lexical Modeling

Kullback-Leibler divergence based hidden Markov model (KL-HMM) is an approach where a posteriori probabilities of phonemes estimated by artificial neural networks (ANN) are modeled directly as feature observation. In this paper, we show the relation between standard HMM-based automatic speech recognition (ASR) approach and KL-HMM approach. More specifically, we show that KL-HMM is a probabilistic lexical modeling approach which is applicable to both HMM/GMM ASR system and hybrid HMM/ANN ASR system. Through experimental studies on DARPA Resource Management task, we show that KL-HMM approach can improve over state-of-the-art ASR system.


Year:
2013
Publisher:
Idiap
Laboratories:




 Record created 2013-12-19, last modified 2018-03-17

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