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Exploring Robustness of DNN/RNN for Extracting Speaker Baum-Welch Statistics in Mismatched Conditions
Hao Zheng; Shanshan Zhang; Wenju Liu
2015
会议名称INTERSPEECH
会议录名称INTERSPEECH
会议日期2015
会议地点Dresden, Germany
摘要This work explores the use of DNN/RNN for extracting Baum-Welch sufficient statistics in place of the conventional GMM-UBM in speaker recognition. In this framework, the DNN/RNN is trained for automatic speech recognition (ASR) and each of the output unit corresponds to a component of GMM-UBM. Then the outputs of network are combined with acoustic features to calculate sufficient statistics for speaker recognition. We evaluate and analyze the performance of networks with different configurations and training corpuses in this paper. Experimental results on text-independent SRE NIST
2008 and text-dependent RSR2015 speaker verification tasks show the robustness of DNN/RNN for extracting statistics in mismatched evaluation conditions compared with GMM-UBM system. Particularly, Long Short-Term Memory (LSTM) RNN realized in this work outperforms traditional DNN and GMM-UBM in most mismatched conditions.
关键词Dnn Rnn Speaker Recognition Mismatched Condition
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11780
专题模式识别国家重点实验室_机器人视觉
通讯作者Hao Zheng
作者单位National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Hao Zheng,Shanshan Zhang,Wenju Liu. Exploring Robustness of DNN/RNN for Extracting Speaker Baum-Welch Statistics in Mismatched Conditions[C],2015.
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