摘要
结合动态谱特性的语音识别研究 ,阐述了一种有限状态矢量量化 (FSVQ)方法。FSVQ利用了过去的信息来选择合适的码本进行编码 ,对于语音识别更为有效。改进了所使用的语音特征参量 ,除了LPC倒谱系数外 ,结合使用了动态谱特征和能量的对数值 ,并根据汉语发音特征对语音信号端点进行一种加权处理。实验结果表明 :与说话人有关的孤立词识别率达到 98%。
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%.
出处
《北京大学学报（自然科学版）》
CAS
CSCD
北大核心
2001年第1期67-70,共4页
Acta Scientiarum Naturalium Universitatis Pekinensis
