APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS. IEEE INTERNATIONAL SYMPOSIUM. 6TH 2011. (SACI 2011) pages:143-146
IEEE International Symposium on Applied Computational Intelligence and Informatics edition:6 location:Timisoara date:19-21 May 2011
Localized spectro-temporal analysis is a novel feature extraction strategy in speech recognition, which was inspired by neurophysiological findings. Here we perform phone recognition experiments on features that are extracted from the patches of the critical-band log-energy
spectrum by applying the two-dimensional cosine trans-form. We find that in phone recognition experiments the proposed feature set yields results similar to the standard MFCC features under clean conditions, while it provides a significantly smaller performance degradation in noisy
conditions. Moreover, we show that the new and the standard features can be readily combined to improve the recognition accuracy still further.