Auditory model based speech recognition in noisy environment

The main purpose of this paper is to present how to raise the speech recognition performance in noisy environment. So far the most popularly used speech feature in speech recognition is probably the so-called MFCC. The recognition rate of speech recognition algorithm using MFCC and CDHMM is known to be very high in clean speech environment, but it deteriorates greatly in noisy environment, especially in the white noisy environment. In this paper, we propose a new speech feature, the ASBF speech feature based on the mathematical model of inner ear of human auditory system. This new speech feature is extracted using both mathematical model of inner ear and primary auditory nerve processing model of human auditory system, and it can track the speech formants effectively. In the experiment, the performance of MFCC and the ASBF are compared in both clean and noisy environments when using left-To-right CDHMM with 6 states and 5 Gaussian mixtures. The experimental result shows that the ASBF is much more robust to noise than MFCC. When only 5 dimension is used in ASBF vector, the recognition rate is approximately 38.6% higher than the traditional MFCC with 39 dimension in the condition of S/N=10dB with white noise.