Abstract：A hybrid model combining the deep neural network (DNN) for speech recognition and the i-vector model for speaker recognition has been shown effective for speaker recognition. The system performance is further improved by using the DNN with speaker labels to extract bottleneck features to replace the original short-term spectral features for statistics extractions to make the statistics contain more speaker-specific information to improve the speaker recognition. Tests on the NIST SRE 2008 female telephone-telephone-English task demonstrate the effectiveness of this method. The relative improvements of the bottleneck features are 7.65% for the equal error rate(EER) and 5.71% for the minium detection function(minDCF) compared with the short-term spectral features.
田垚, 蔡猛, 何亮, 刘加. 基于深度神经网络和Bottleneck特征的说话人识别系统[J]. 清华大学学报（自然科学版）, 2016, 56(11): 1143-1148.
TIAN Yao, CAI Meng, HE Liang, LIU Jia. Speaker recognition system based on deep neural networks and bottleneck features. Journal of Tsinghua University(Science and Technology), 2016, 56(11): 1143-1148.
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