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.

In  IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, ICASSP 2011

Publisher  IEEE International Confrence on Acoustics, Speech, and Signal Processing (ICASSP)

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TypeInproceedings
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