Order adaptation of the fractional Fourier transform using the intraframe pitch change rate for speech recognition

We propose an acoustic feature for speech recognition based on the combination of MFCC and fractional Fourier transform (FrFT). The transform orders for FrFT are adaptively set according to the intraframe pitch change rate. This method is motivated by the fact that the speech is not stationary even in a short period of time, and the idea is shown using an AM-FM speech model and some spectrograms of an artificial periodic signal. Experiments were conducted on the intervocalic English consonants provided by Interspeech 2008 Consonant Challenge and a Mandarin connected digits corpus. The performance of the proposed method is compared with the MFCC baseline system. Experimental results show that the proposed features get a slightly better recognition rate than MFCCs presumably because they can better track the dynamic characteristics of the speech harmonics.