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A Study of Discriminative Feature Extraction for i-vector Based Acoustic Sniffing in IVN Acoustic Model Training

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

Abstract

Recently, we proposed an i-vector approach to acoustic sniffing for irrelevant variability normalization based acoustic model training in large vocabulary continuous speech recognition (LVCSR). Its effectiveness has been confirmed by experimental results on Switchboard-1 conversational telephone speech transcription task. In this paper, we study several discriminative feature extraction approaches in i-vector space to improve both recognition accuracy and run-time efficiency. New experimental results are reported on a much larger scale LVCSR task with about 2000 hours training data.

Details

Publication typeInproceedings
Published inIEEE International Conference on Acoustics, Speech and Signal Processing, 2012, ICASSP 2012
URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6288814
PublisherInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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