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 [BibTeX] [Marc21]
KL-HMM and Probabilistic Lexical Modeling
Type of publication: Idiap-RR
Citation: Rasipuram_Idiap-RR-04-2013
Number: Idiap-RR-04-2013
Year: 2013
Month: 2
Institution: Idiap
Abstract: 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.
Projects Idiap
Authors Rasipuram, Ramya
Magimai.-Doss, Mathew
Added by: [ADM]
Total mark: 0
  • Rasipuram_Idiap-RR-04-2013.pdf (MD5: 43347eaa99db9034519d4cfa77038946)