Subband Correlation and Robust Speech Recognition

This paper investigates the effect of modeling subband correlation for noisy speech recognition. Subband feature streams are assumed to be independent in many subband-based speech recognition systems. However, speech recognition experimental results suggest this assumption is unrealistic. In this paper, a method is proposed to incorporate correlation into subband speech feature streams. In the proposed method, all possible combinations of subbands are created and each combination is treated as a single frequency-band by calculating a single feature vector for it. The resulting feature vectors, therefore, capture information about every band in the combination, as well as the dependency across the bands. Although using the new features results in a higher computational complexity, our experimental results show that they effectively capture the correlation between the subbands while making minimal assumptions about the structure of the correlation. Experiments are conducted on the TIDigits database. The results demonstrate improved accuracy for clean speech recognition and improved robustness in the presence of both stationary and nonstationary band-selective noise, in comparison to a system assuming subband independence.