该文报告了组合LPC参数以及基频F0的高斯混合模型(GMM)电话语音说话人自动识别技术的实验研究结果.该研究在基线试验中GMM使用16混合共分散对角矩阵,特征量为LPC倒谱系数.而在开发系统测试中分别利用语音的全发话区间和有声区间两部分参数增加基频参数进行试验,并给出实验比较结果.在50人电话通话开放集自动切分语音流实验中正确识别率为76.97%,而提案方法为80.29%,改善率为3.32%.接近人工切分语音流时的识别率82.34%.%This paper describes a free calling speaker recognition system based on GMM (Gaussian Mixtures Model) using LPC and Pitch information from spontaneous speech. The base line system in our test uses a GMM with 8-gaussian mixtures with diagonal covariance matrix, and for acoustic feature vector, the LPC cepstrum coefficient are used. In addition, the fundamental frequency (f0) are added to the PLC cepstrum for voiced part of speech signal. The experimental results show the speaker recognition rate of 76. 97% on the base line test, and 80. 29% on proposed approach, respectively, among the speech data from 50 sneakers. This result is close to the rate of 82. 34% of speaker recognition using the speech data by manual segmentation.
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