Phase autocorrelation (PAC) features in entropy based multi-stream for robust speech recognition

Methods to improve noise robustness of speech recognition systems often result in degradation of recognition performance for clean speech. Recently proposed phase autocorrelation (PAC) based features (S. Ikbal et al., Proc. ICASSP-03, p.II-133-6, 2003; Proc. IEEE ASRU 2003 Workshop, 2003), while showing noticeable improvement in noise robustness, also suffer from this drawback. We try to alleviate this problem by using the PAC based features along with regular speech features in a multi-stream framework. The multi-stream system uses the entropy of the posterior probability distribution, computed during recognition, as a confidence measure to combine evidence from different feature streams adaptively (Misra, H. et al., Proc. ICASSP-03, p.II-741-4, 2003). Experimental results obtained on OGI Numbers95 database and Noisex92 noise database show that such a system yields the best possible recognition performance in all conditions. Actually, the combination always performs better than the best performing stream for all the conditions.