Subband feature extraction using lapped orthogonal transform for speech recognition

It is well known that dividing speech into frequency subbands can improve the performance of a speech recognizer. This is especially true for the case of speech corrupted with noise. Subband (SUB) features are typically extracted by dividing the frequency band into subbands by using non-overlapping rectangular windows and then processing each subband s spectrum separately. However, multiplying a signal by a rectangular window creates discontinuities which produce large amplitude frequency coefficients at high frequencies that degrade the performance of the speech recognizer. In this paper we propose the lapped subband (LAP) features which are calculated by applying the discrete orthogonal lapped transform (DOLT) to the mel-scaled, log-filterbank energies of a speech frame. Performance of the LAP features is evaluated on a phoneme recognition task and compared with the performance of SUB features and MFCC features. Experimental results show that the proposed LAP features outperform SUB features and mel frequency cepstral coefficients (MFCC) features under white noise, band-limited white noise and no noise conditions