Robust Feature Extraction for Speech Recognition Based on Perceptually Motivated MUSIC
A novel feature extraction algorithm was proposed aiming at improving speech recognition rate in noise environmental conditions. Core technology was the Multiple Signal Classification (MUSIC), which estimated MUSIC spectrum from the speech signal and incorporated perceptual information directly into the spectrum estimation, then the cepstrum coefficients were extracted as the feature parameter. We evaluated the technique using improved Hidden Markov Model (H.MM) in different noisy environment, six Chinese vowels were taken as the experimental data. The experimental results show that the novel feature has very good robustness and effectiveness relative to the previously proposed Mel Frequency Cepstral Coefficient (MFCC) technique and the improved HMM can make speech recognition system robust in noise environmental conditions.
speech recognition feature extraction multiple signal classification(MUSIC) hidden markov model(HMM) genetic algorithm(GA)
HAN Zhi-yan WANG Jian
College of Information Science & Engineering Bohai University Jinzhou Liaoning, China
国际会议
成都
英文
98-102
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）