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首页> 外文会议>International Multi-Conference on Systems, Signals Devices >Improved closed set text independent speaker identification system using Gammachirp Filterbank in noisy environments
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Improved closed set text independent speaker identification system using Gammachirp Filterbank in noisy environments

机译：在嘈杂的环境中使用Gammachirp Filterbank改进的闭集独立于文本的说话人识别系统

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Several modern speaker recognition systems use a bank of linear filters as the primary step in performing frequency analysis of speech and extracting the acoustics parameters that permit characterizing the speaker identity. In this paper we point up the employ of novel feature set extracted from speech signal. The new skill for extracting these parameters is based on the human auditory system characteristics and relies on the Gammachirp Filterbank to imitate the cochlea frequency resolution with nonlinear resolution according to the equivalent rectangular bandwidth (ERB) scale. For evaluation a comparative study was operated with standard MFCC, and the effect of these differences using an usual HMM/GMM for text independent speaker recognition system, for noisy environments. Performances were test database contaminated with additive noise different real-environment noises were used: car noise provided by Volvo, factory noise and white noise from Noisex92 [1]. Tests were carried out at different SNR levels (−3dB, 0dB, 3dB, 6dB, 12dB).
机译：几种现代的说话人识别系统将一组线性滤波器用作执行语音频率分析和提取允许表征说话人身份的声学参数的主要步骤。在本文中，我们指出了从语音信号中提取的新颖特征集的应用。提取这些参数的新技能是基于人类听觉系统的特征，并依赖于Gammachirp滤波器组根据等效矩形带宽（ERB）比例模拟具有非线性分辨率的耳蜗频率分辨率。为了进行评估，使用标准的MFCC进行了比较研究，对于嘈杂的环境，使用普通的HMM / GMM进行了独立于文本的说话者识别系统，以评估这些差异的影响。性能测试数据被添加噪声污染，使用了不同的实际环境噪声：沃尔沃提供的汽车噪声，工厂噪声和Noisex92产生的白噪声[1]。测试是在不同的SNR级别（-3dB，0dB，3dB，6dB，12dB）下进行的。

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