A comparison between hidden Markov models and vector quantization for speech independent speaker recognition

1993 IEEE South African Symposium on Communications and Signal Processing Pub Date : 1993-08-06 DOI:10.1109/COMSIG.1993.365856

D. Weber, J. du Preez

引用次数: 11

Abstract

We compare Vector Quantization and Hidden Markov Models for speaker recognition for real time recognition. A scheme to reject speakers not known to the system is described and tested. Results show that the HMM algorithm outperforms the VQ algorithm. Using a 64 state HMM, a speaker recognition accuracy of 96.1% was achieved. The rejection option generated 25.7% false rejections for a 95% confidence of a correct decision. VQ best results were 93.1% with a 61% false rejection rate for codebooks of size 128.<>

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隐马尔可夫模型与矢量量化在语音独立说话人识别中的比较

我们比较了矢量量化和隐马尔可夫模型用于说话人识别的实时识别。描述并测试了一种拒绝系统不认识的说话者的方案。结果表明，HMM算法优于VQ算法。采用64态HMM，对说话人的识别准确率达到96.1%。拒绝选项产生了25.7%的错误拒绝，而正确决策的置信度为95%。对于大小为128的码本，VQ的最佳结果为93.1%，错误拒绝率为61%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

1993 IEEE South African Symposium on Communications and Signal Processing

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