关键词: 声纹识别, 数据驱动, 缺失特征重建, 噪声鲁棒性, 子带信噪比

Abstract: Pointing at improving the noise robustness of speaker recognition systems, a front-end processing method for voiceprint recognition based on data training missing feature detection and reconstruction is proposed. Taking full advantage of the a priori information obtained from massive training data, this method can accurately calculate subband SNR, detect, mark and reconstruct the subband feature parameters which are serious polluted by various noise, in this way, noise robust feature parameter is obtained. Experiments show that in low SNR environment, data training method has achieved a high recognition rate, and system performance also improved in none stationary environment.

Key words: voice print recognition, data training, missing feature reconstruction, noise robustness, sub-band SNR