Varying Time Constants and Gain Adaptation in Feature Extraction for Speech Processing

Previously we showed that band-pass filtered MFCC-like features are useful for noise robust speech discrimination and recognition. In this paper we aim to improve the previously presented features by incorporating varying time constants and gain adaptation in each frequency channel. We show that varying the time constants leads to a representation that is less prone to the effects of noise. Further, we show that gain adaptation can not only provide better performance in clean condition but can also be used to improve the noise robustness of the features. These improvements come at a very small increase in computational cost. Speech discrimination and recognition results are presented.