关键词: 腭裂语音, 声韵母切分, 层次聚类, 短时自相关函数, 类浊音权重函数, 类清音概率函数, 短时幅度函数

Abstract: This paper presents an initial/final segmentation algorithm in cleft palate speech. Through subjective test and objective F test and t test, it is proven that there are significant differences between cleft palate speech and normal speech. Two types of syllables are defined firstly：Class I syllable whose initial has the characteristics of voiceless phoneme, and Class II syllable whose initial has the characteristics of voiced phoneme. These two types of syllables are classified based on hierarchical fuzzy clustering model. Then for the class I syllable, a similar-to-voiced-sound weighting function and similar-to-unvoiced-sound probability function are defined, in order to achieve the roughly initial/final segmentation. The accurate location of initial/final boundary in class I syllable is achieved, through calculating the first-order difference of autocorrelation function’s peak number. For the class II syllable, based on the waveform difference between initial and final, the energy’s jumping point of short-autocorrelation function is found in order to achieve the initial/final segmentation. The experiment results show that the proposed algorithm achieves a high segmentation accuracy rate：Segmentation accuracy for class I syllables is 90.72%, and segmentation accuracy for class II syllables is 92.90%.

Key words: cleft palate speech, initials and finals segmentation, hierarchical clustering, short-time autocorrelation, similar-to-voiced-sounds weighting function, similar-to-unvoiced-sound probability function, short-time magnitude function