基于双通道特征的含噪声纹识别方法研究

A STUDY OF VOICEPRINT RECOGNITION WITH NOISE BASED ON DUAL-CHANNEL VOICEPRINT FEATURE

  • 摘要: 针对声纹识别在噪声环境下准确率不高的问题,提出一种基于双通道声纹特征(Dual-Channel Voice-print Feature, DCVF)的含噪声纹识别方法。将处理后的语音信号分别通过梅尔滤波器组和伽马通滤波器组,得到梅尔频率倒谱系数特征、伽马通倒谱系数特征,结合它们的差分谱构成混合特征,融合成双通道声纹特征(DCVF)。实验结果表明:在纯净语音数据集中,双通道声纹特征可达到99.5%的识别率;在含噪语音数据集下,DCVF的识别效果有明显提升。

     

    Abstract: Aimed at the low accuracy of voiceprint recognition under noisy environment, a dual-channel voiceprint feature (DCVF) is proposed. After the processed speech signal was passed by the Mel filter group and the Gammatone filter group, the Mel frequency cepstral coefficients (MFCC) and the Gammatone frequency cepstral coefficient (GFCC) was obtained. The mixed feature was formed by combining their dynamic differential spectrum and fusing them to form dual-channel voiceprint feature (DCVF). Experimental results show that the dual-channel voiceprint can achieve 99.5% recognition rate in pure speech data sets. Compared with MFCC and GFCC, DCVF is significantly improved on noisy speech data sets.

     

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