Mixture weight optimization for dual-microphone MFCC combination

Feature combination in the MFCC domain can improve speech recognition accuracy of dual-microphone systems, if two microphones have different characteristics. If we can take advantage of the recognition hypotheses given by single channel decoding, cross-domain feature combination between the MFCC domain and the hypothesis domain provides better results than simple weighted average of MFCCs. However, it is problematic that the optimal mixture weight is unknown. In this paper, we propose to use the channel selection algorithm for mixture weight optimization, regarding various parameter values as separate channels. Evaluation experiments show that the recognition performance can be greatly improved when we use hypothesis-based feature combination and decoder-based channel selection