关键词: 有限状态矢量量化, LPC倒谱系数, 动态谱特性, 动态规整, 状态转移函数

Abstract: A speech recognition method is described, that is based on a combination of finite-state vector quantization(FSVQ) and dynamic spectral features. FSVQ is a recallable vector quantization system, which also uses past information for optimizing the code book, and is more effective for speech recognition. The characteristics of a speech signal are represented by time sequences of LPC cepstral coefficients, the dynamic spectral features and log-energy. According to pronunciation feature of Mandarin, the distance values were weighted for the parts of word termination. The experimental results show that the depended speaker speech recognition rate is 98%.

Key words: finite-state vector quantization, LPC cepstral coefficients, dynamic spectral feature, dynamic time warpping, state transition function