Data Driven Design of Filter Bank for Speech Recognition

Abstract

Filter bank approach is commonly used in feature extraction phase of speech recognition (e.g. Mel frequency cepstral coefficients). Filter bank is applied for modification of magnitude spectrum according to physiological and psychological findings. However, since mechanism of human auditory system is not fully understood, the optimal filter bank parameters are not known. This work presents a method where the filter bank, optimized for discriminability between phonemes, is derived directly from phonetically labeled speech data using Linear Discriminant Analysis. This work can be seen as another proof of the fact that incorporation of psychoacoustic findings into feature extraction can lead to better recognition performance.