The aim of this work is to develop a practical framework, which extends the classical Hidden Markov Models (HMM) for continuous speech recognition based on the Maximum Entropy (MaxEnt) principle. The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist speech recognition systems. In particular, a new acoustic modelling based on discriminative MaxEnt models is formulated and is being developed to replace the generative Gaussian Mixture Models (GMM) commonly used to model acoustic variability. Initial experimental results using the TIMIT phone task are reported.
Cite as: Hifny, Y., Renals, S., Lawrence, N.D. (2005) A hybrid Maxent/HMM based ASR system. Proc. Interspeech 2005, 3017-3020, doi: 10.21437/Interspeech.2005-143
@inproceedings{hifny05_interspeech,
author={Yasser Hifny and Steve Renals and Neil D. Lawrence},
title={{A hybrid Maxent/HMM based ASR system}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={3017--3020},
doi={10.21437/Interspeech.2005-143}
}