Statistical models for HMM/ANN hybrids

We present a theoretical investigation into the use of normalised artificial neural network (ANN) outputs in the context of hidden Markov models (HMMs). The work is motivated by the pursuit of a more theoretically rigorous understanding of the Kullback-Liebler (KL)-HMM. Two possible models are considered based respectively on the HMM states storing categorical distributions and Dirichlet distributions. Training and recognition algorithms are derived, and possible relationships with KL-HMM are briefly discussed

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