基于音节时间长度高斯拟合的汉语音节切分方法

Chinese speech segmentation method based on Gauss distribution of time spans of syllables

研究汉语自然语音音节切分方法具有明显现实意义,比较准确的自然语音切分方法可以代替人工对一些拥有参照文本的语音进行标注。然而至今为止并没有完全准确的汉语语音音节切分方法。依据相同发音环境下汉语语音音节时间长度服从某种高斯分布和相邻语音音节之间存在短时能量波谷两个假设,提出了基于音节时间长度高斯拟合的汉语音节切分方法。对算法进行分析,根据初步切分短时能量波谷分散到各分语音段的特性,提出了简化算法,有效降低了该音节切分方法的时间复杂度。实验结果表明,音节切分准确度(与人工标注切分时间距离平方的均值)达到小数点后3位,在台式机Matlab环境下运算时间均不超过1 s,可以达到应用要求。

So far away,there is no accurate method for Chinese natural speech segmentation of syllables,which is meaningful in labeling speech with reference text instead of people. According to two hypotheses that time spans of Chinese syllables under the same pronunciation obey Gauss distribution and short-time energy valley exists between two adjacent syllables,Chinese speech segmentation method based on Gauss distribution of time spans of syllables was proposed. A simplified method based on distribution of energy valleys was given,which effectively reduced the time complexity of this speech segmentation method. The experimental results show that segmentation accuracy（ mean square value of time spans between artificial labels and labels created by this method） achieve 10- 3and computing times are less than 1 s in Matlab of PC.