汉语语音的聚类分段研究

CLUSTERING SEGMENTATION OF CHINESE SPEECH

本文提出了一种新的聚类分段算法,这个算法以段内平均离散度最小、段间平均离散度最大为准则,采用聚类的方法逐次迭代选择最佳分段断点和分段段数,能正确地对汉语语音进行音素分段,它和以往分段方法相比在性能上有很大提高.文中还给出了应用该算法对汉语单音所作的部分实验统计结果,可供进一步开展基于音素或音位的汉语语音识别研究参考.

This paper proposes a novel clustering segmentation algorithm. Based on the criterion of the minimization of average dispersion within segments and the maximization of average dispersion between segments, the optimum speech segmentation has been approached by this clustering method. Compared with former classical methods, this algorithm has improved the performance in the phoneme segmentation of Chinese syllables significantly.The statistical results of some preliminary experiments for isolated Chinese words are reported, which can be used as reference in the phonemes recognition of Chinese speech.