A Multistream Feature Framework Based on Bandpass Modulation Filtering for Robust Speech Recognition

There is strong neurophysiological evidence suggesting that processing of speech signals in the brain happens along parallel paths which encode complementary information in the signal. These parallel streams are organized around a duality of slow vs. fast: Coarse signal dynamics appear to be processed separately from rapidly changing modulations both in the spectral and temporal dimensions. We adapt such duality in a multistream framework for robust speaker-independent phoneme recognition. The scheme presented here centers around a multi-path bandpass modulation analysis of speech sounds with each stream covering an entire range of temporal and spectral modulations. By performing bandpass operations along the spectral and temporal dimensions, the proposed scheme avoids the classic feature explosion problem of previous multistream approaches while maintaining the advantage of parallelism and localized feature analysis. The proposed architecture results in substantial improvements over standard and state-of-the-art feature schemes for phoneme recognition, particularly in presence of nonstationary noise, reverberation and channel distortions.