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首页> 外文期刊>Journal of Information Security >Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models
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Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models

机译：基于Mel倒谱系数和高斯混合模型的文本独立说话人自动识别系统

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摘要

The aim of this paper is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture Models (GMM), in order to develop a security control access gate. 450 speakers were randomly extracted from the Voxforge.org audio database, their utterances have been improved using spectral subtraction, then MFCC were extracted and these coefficients were statistically analyzed by GMM in order to build each profile. For each speaker two different speech files were used: the first one to build the profile database, the second one to test the system performance. The accuracy achieved by the proposed approach is greater than 96% and the time spent for a single test run, implemented in Matlab language, is about 2 seconds on a common PC.
机译：本文的目的是展示基于梅尔倒谱倒谱系数（MFCC）和高斯混合模型（GMM）的文本独立自动说话人识别（ASR）系统的准确性和时间结果，以便开发安全控制检修门。从Voxforge.org音频数据库中随机提取了450个说话者，使用频谱相减法改善了他们的话语，然后提取了MFCC，并通过GMM对这些系数进行了统计分析，以建立每个配置文件。对于每个发言人，使用了两个不同的语音文件：第一个用于建立配置文件数据库，第二个用于测试系统性能。所提出的方法所达到的精度大于96％，并且在Matlab语言上实现的单次测试所花费的时间在普通PC上约为2秒。

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