INTERSPEECH 2008
9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Confusion-Based Entropy-Weighted Decoding for Robust Speech Recognition

Yi Chen, Chia-yu Wan, Lin-shan Lee

National Taiwan University, Taiwan

An entropy-based feature parameter weighting scheme was proposed previously [1], in which the scores obtained from different feature parameters are weighted differently in the decoding process according to an entropy measure. In this paper, we propose a more delicate entropy measure for this purpose considering the inherent confusion among different acoustic classes. If a set of acoustic classes are easily confused, those feature parameters which can distinguish them should be emphasized. Extensive experiments with the Aurora 2 testing environment verified that this approach is equally useful for different types of features, and can be easily integrated with typical existing robust speech recognition approaches.

Reference

  1. Y. Chen, C.-Y. Wan, L.-S. Lee, "Entropy-Based Feature Parameter Weighting for Robust Speech Recognition," ICASSP 2006.

Full Paper

Bibliographic reference.  Chen, Yi / Wan, Chia-yu / Lee, Lin-shan (2008): "Confusion-based entropy-weighted decoding for robust speech recognition", In INTERSPEECH-2008, 1008-1011.