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A study of discriminative feature extraction for i-vector based acoustic sniffing in IVN acoustic model training

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4 Author(s)
Yu Zhang ; Microsoft Res. Asia, Beijing, China ; Jian Xu ; Zhi-Jie Yan ; Qiang Huo

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.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012