Design of Speech Control System IN Car Noise Environments

摘要

Presence of additive noise in speech signals deteriorates the performance of automatic speech recognition systems in cars. For a speech recognition system, we must know where speech and nonspeech segments are. In this paper a new Band Partitioning Spectral Entropy endpoint detection (BPSE) method is used to get the speech start and end point of speech precisely. After that Band Spectral Subtraction (BSS) methods provide in this paper can decrease additive noise obviously. Mel Frequency Cepstral Coefficients (MFCC) are extracted from segmented speech signals. The coefficients are recognized by Hidden Markov Model. The results show that the recognition accuracy can be improved from 39.3% to 95.5%.