Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
A Study of an Irrelevant Variability Normalization Based Discriminative Training Approach for LVCSR

Yu Zhang, Jian Xu, Zhi-Jie Yan, and Qiang Huo

Abstract

This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large vocabulary continuous speech recognition. A speaker-clustering based method is used for acoustic sniffing and maximum mutual information (MMI) is used as a training criterion. Combined with unsupervised adaptation of feature transforms, the IVN-based DT approach achieves a 14.5% relative word error rate reduction over an MMI-trained baseline system on a Switchboard-1 conversational telephone speech transcription task.

Details

Publication typeInproceedings
Published inIEEE International Conference on Acoustics, Speech and Signal Processing, 2011, ICASSP 2011
PublisherIEEE International Confrence on Acoustics, Speech, and Signal Processing (ICASSP)
> Publications > A Study of an Irrelevant Variability Normalization Based Discriminative Training Approach for LVCSR