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@TECHREPORT{Rasipuram_Idiap-RR-04-2013,
         author = {Rasipuram, Ramya and Magimai.-Doss, Mathew},
       projects = {Idiap},
          month = {2},
          title = {KL-HMM and Probabilistic Lexical Modeling},
           type = {Idiap-RR},
         number = {Idiap-RR-04-2013},
           year = {2013},
    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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Rasipuram_Idiap-RR-04-2013.pdf}
}