一种基于语音组成单位的说话人识别算法

以线性预测系数为特征通过高斯混合模型的迭代算法对训练样本的初始k均值聚类结果进行优化,得到语音组成单位的表示.以语音组成单位的模式匹配为基础,提出一种文本无关说话人确认的方法——均值法,以及一种文本无关说话人辨认方法.实验结果表明,即使在短时语音下本文方法都能取得较好效果.

A Speaker Recognition Algorithm Based on Speech Component Unit

The linear prediction coefficients (LPC) are selected as features to construct the corresponding feature space. Firstly, the samples for training of each individual in system are clustered in the feature space to form initial clusters by using k-mean clustering algorithm. Then, the initial clustering results are optimized based on Gaussian mixture model (GMM) iterative algorithm to obtain the speech component unit representation of the individual. On the basis of the obtained speech component units, a text-independent speaker verification method, called averaging method, and a text-independent speaker identification method are presented. Experimental results show that the proposed algorithm can produce a satisfying result even in short utterances.