Unknown-multiple signal source clustering problem using ergodic HMM and applied to speaker classification
Unknown-multiple signal source clustering problem using ergodic HMM and applied to speaker classification
复制标题
使用遍历 HMM 的未知多信号源聚类问题并应用于说话人分类
DOI：
10.1109/icslp.1996.607294
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
Hideyuki Watanabe
中科院分区：
文献类型：
--
作者：
J. Murakami;M. Sugiyama;Hideyuki Watanabe
作者认为信号起源于一系列的来源。更具体地说，对这些信号进行分段并将分段与其源联系起来的问题得到了解决。这个问题在许多领域都有广泛的应用。本文描述了一种基于遍历隐马尔可夫模型（HMM）的分辨率方法，其中每个隐马尔可夫模型状态对应一个信号源。信号源序列可以通过在观察到的序列上使用解码程序（维特比算法或前向算法）来确定。Baum-Welch训练用于从训练材料中估计HMM参数。以多信号源分类问题为例，进行了未知说话人分类的实验。结果显示，4名男性说话者的分类率为79%。结果还表明，该模型对遍历隐马尔可夫模型的初始值敏感，采用长距离LPC倒谱对信号进行预处理是有效的。
The authors consider signals originated from a sequence of sources. More specifically, the problems of segmenting such signals and relating the segments to their sources are addressed. This issue has wide applications in many fields. The report describes a resolution method that is based on an ergodic hidden Markov model (HMM), in which each HMM state corresponds to a signal source. The signal source sequence can be determined by using a decoding procedure (Viterbi algorithm or forward algorithm) over the observed sequence. Baum-Welch training is used to estimate HMM parameters from the training material. As an example of the multiple signal source classification problem, an experiment is performed on unknown speaker classification. The results show a classification rate of 79% for 4 male speakers. The results also indicate that the model is sensitive to the initial values of the ergodic HMM and that employing the long-distance LPC cepstrum is effective for signal preprocessing.