CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
An SAD Algorithm based on SGMM and Phoneme Combination
Chen, Xiao; Xu, Bo
2015-12
Conference NameInternational Conference on Computer Science and Network Technology
Source PublicationProceedings of the Fourth International Conference on Computer Science and Network Technology
Conference Date19-20
Conference PlaceHarbin
Abstract
Speech activity detection (SAD) is the key preprocess of speech application. This paper proposed a subspace Gaussian mixture model (SGMM) and phoneme combination based SAD algorithm. This algorithm is efficient, small and can utilize speech recognition corpus directly. Results indicate that, compared with the baseline, our proposed method results in an absolute improvement of 4.9% frame error rate and 10% average hit rate, respectively. Our approach finally achieves a frame error rate of 5.1% and an average hit rate of 91.5%. The model size is just 809.5K.

 
KeywordSpeech Activity Detection Subspace Gaussian Mixture Model Phoneme Combination
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11824
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorChen, Xiao
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, Xiao,Xu, Bo. An SAD Algorithm based on SGMM and Phoneme Combination[C],2015.
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