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首页> 外文期刊>Indian Journal of Science and Technology >Speaker Identification System using Gaussian Mixture Model and Support Vector Machines (GMM-SVM) under Noisy Conditions
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Speaker Identification System using Gaussian Mixture Model and Support Vector Machines (GMM-SVM) under Noisy Conditions

机译：噪声条件下使用高斯混合模型和支持向量机（GMM-SVM）的说话人识别系统

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Background: Automatic Speaker Identification (SID) systems has been a major breakthrough and crucial in many real world applications. Methods: This work addresses the SID task based on GMM-SVM in a three stage process. Firstly, the Gammatone Frequency Cepstral Coefficients (GFCC) and Mean Hilbert Envelope Coefficients (MHEC) of the speakers are extracted. Secondly, these features are modeled using Gaussian Mixture Model (GMM), on adapting the extracted acoustic features by mean, the corresponding super vectors are found and these vectors are trained using Support Vector Machine (SVM). Finally, the actual recognition is done by feeding the super vectors of them asked noisy test utterance by Ideal Binary Mask (IBM) into SVM model and their accuracy of recognition is compared for GFCC, MHEC and RASTA-MFCC in different noisy conditions. Findings: Evaluation results show that SID performance carried out with MHEC is extensively better than the performance of other two features. Applications: Major areas that implements automatic SIDs are forensics, surveillance and audio biometrics etc.
机译：背景：说话人自动识别（SID）系统已成为重大突破，并且在许多实际应用中至关重要。方法：这项工作分三个阶段解决了基于GMM-SVM的SID任务。首先，提取扬声器的伽马通频率倒谱系数（GFCC）和平均希尔伯特包络系数（MHEC）。其次，使用高斯混合模型（GMM）对这些特征进行建模，通过平均地适应提取的声学特征，找到相应的超向量，并使用支持向量机（SVM）训练这些向量。最后，通过将理想二进制蒙版（IBM）所要求的有噪声测试话语的超向量馈入SVM模型来完成实际识别，并比较了在不同噪声条件下GFCC，MHEC和RASTA-MFCC的识别精度。结果：评估结果表明，使用MHEC进行的SID性能远远优于其他两个功能。应用范围：实施自动SID的主要领域是取证，监视和音频生物识别等。

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