计算机工程与应用2016，Vol.52Issue(22)：159-163,5.DOI:10.3778/j.issn.1002-8331.1501-0091
基于数据驱动缺失特征检测与重建的声纹识别
Voice print recognition based on data training missing feature detec-tion and reconstruction
摘要
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.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Key words
voice print recognition/data training/missing feature reconstruction/noise robustness/sub-band SNR分类
信息技术与安全科学引用本文复制引用
尹海明,王金明,李欢欢..基于数据驱动缺失特征检测与重建的声纹识别[J].计算机工程与应用,2016,52(22):159-163,5.基金项目
中兴通讯产学研合作研究项目资助（No.CON1307160001）。 （）
