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

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.

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

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

Details

TypeInproceedings
URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6288814
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