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Speaker identification using vector quantization and I-vector with reference to Assamese language

机译:使用矢量量化和I形式的扬声器识别,参考assameese语言

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This paper describes the implementation of a speaker identification system with reference to Assamese language. The database consists of speech samples that were collected from 15 (fifteen) speakers for ten Assamese words representing the Assamese digits from 0 (shounyo) to 9 (no). Mel Frequency Cepstral Coefficients (MFCC) are used as features for this study. Two independent speaker identification systems have been built in this paper using Vector Quantization (VQ) and I-vector technique. The system built using the I-vector technique obtains comparatively better identification accuracy for speakers when compared with the system developed using VQ technique. Three different systems have been built for both the techniques based on variable feature size. A maximum accuracy of 92.38% is achieved using I-vector technique with 39 MFCC features.
机译:本文介绍了参考assamene语言的扬声器识别系统的实现。该数据库由演讲样本组成,这些语音样本从15个(十五)扬声器收集,以获得从0(Shounyo)到9(否)的issamese数字。 MEL频率患者患者系数(MFCC)用作本研究的特征。本文使用了矢量量化(VQ)和I载体技术,建立了两个独立的扬声器识别系统。与使用VQ技术开发的系统相比,使用I-Vector Techne技术构建的系统对扬声器进行了相对更好的识别准确性。基于变量特征大小的技术建立了三种不同的系统。使用具有39个MFCC功能的I载体技术实现了92.38 %的最大精度。

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