CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Exploring Robustness of DNN/RNN for Extracting Speaker Baum-Welch Statistics in Mismatched Conditions
Hao Zheng; Shanshan Zhang; Wenju Liu
2015
Conference NameINTERSPEECH
Source PublicationINTERSPEECH
Conference Date2015
Conference PlaceDresden, Germany
AbstractThis 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.
KeywordDnn Rnn Speaker Recognition Mismatched Condition
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11780
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorHao Zheng
AffiliationNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
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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