A study of an irrelevant variability normalization based discriminative training approach for LVCSR

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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Yu Zhang, Jian Xu, Zhi-Jie Yan, Qiang Huo
• Keywords: Feature Transforms, ICASSP 2011, Irrelevant Variability Normalization, IVN-based DT Approach, Signal Processing