Zero-Crossings with Adaptation for Automatic Speech Recognition

An auditory model based on zero-crossings with peak amplitudes (ZCPA) was used as a front-end for automatic speech recognition (ASR) with the perceptual property of adaptation as determined by psychoacoustic observations. The model performance was evaluated on the isolated digits (TIDIGITS) database using continuous density HMM recognizer in additive noise. Experimental results indicate that the ASR performance of the ZCPA may be improved with adaptation over the static baseline performance in white Gaussian and factory noise. The perceptual front-end was also evaluated with dynamic (delta and delta-delta) features added to the adaptation. It was observed that adaptation with dynamic features performed better in factory, babble and car noise over a wide range of SNR values. The recognition performances were compared with the baseline MFCC. The performance of the dynamic ZCPA with adaptation was better than the dynamic MFCC in white Gaussian noise.