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中文核心期刊

基于模糊神经网络的话者辩认研究

Speaker identification based on fuzzy-neural networks

  • 摘要: 本文首先根据衡量话者特征参数有效性的F比值公式分析比较了LSP和CEP参数在描述话者特征方面的不同特性,确定LSP参数作为话者特征参数.接着提出了描述话者特征的层次模型.根据函数扩展网络的内插能力和隶属函数的模糊统计建立方法,用函数扩展网络实现模糊状态的隶属函数,由网络隶属函数构成话者特征层次模型的基本单元.以最大隶属度原则作为系统的辨认决策准则.在小词表(0~9十个数字)内,采用文本无关的方式进行42人的话者辨认实验,当测试语音由5个数字随机组合时系统正确辨认率为99.76%.

     

    Abstract: In this paper, F ratio formula, which is used to measure the effectiveness of the parameters for representing speaker individual features, is employed to compare the effectiveness of LSP frequencies with that of CEP parameters. Some specific properties have been drawn from the so two kinds of parameters. Based on this work, LSP frequencies are determined as individual features. Then a hierarchical model for describing individual features of each speaker is proposed. According to the capability of interpolation with functional——link networks and the fuzzy statistical method of establishing membership functions, the membership functions of the fuzzy states are implemellted with such networks which are the basic units of the hierarchical model. During the identification procedure, the maximum degree of membership fUnction to speaker's model is used as the decision criterion. In small fixed vocabulary, ten Chinese digital voices (0-9), text-independent speaker identification test are carried out among 42 speakers. When the length of testing utterances is randomly concatenated with 5 digital voices, the correct identification rate is 99.76 percent.

     

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