%Aigaion2 BibTeX export from Idiap Publications %Friday 13 December 2024 11:38:56 PM @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} }