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Research of whispered speech vocal tract system conversion based on universal background model and effective Gaussian components 被引量:1
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作者 CHEN Xueqin ZHAO Heming 《Chinese Journal of Acoustics》 2013年第4期400-410,共11页
Directing to the weakness of the present fixed values mapping methods (method_F), a vocal tract system conversion method based on the universal background model (UBM) is proposed for improving the performance of t... Directing to the weakness of the present fixed values mapping methods (method_F), a vocal tract system conversion method based on the universal background model (UBM) is proposed for improving the performance of the speech conversion system from Chinese whis- pered speech to normal speech. For the numerous components of UBM, the errors produced by the acoustical probability density statistical model can't be ignored. Thus an effective Gaus- sian mixture components chosen method based on the posterior probability summation of the minimum spectral distortion is developed to optimizing the system performance. The proposed method (method_U) is analyzed and compared using the performance index (PI) based on Itakura-Saito spectral distortion measure. It is shown experimentally that the performance of method_U is more stability for different speakers and different phonemes than that of method_F. The average PI of method_U is better than method_F. It is shown that by selecting effective Gaussian mixture components, the PI of method_U can be further improved 5.11%. Subjective auditory tests also show that the proposed method can improve the definition and intelligibility of conversion speech. 展开更多
关键词 Research of whispered speech vocal tract system conversion based on universal background model and effective Gaussian components UBM
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A Recommendation Mechanism for Web Publishing Based on Sentiment Analysis of Microblog 被引量:2
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作者 TIAN Pingfang ZHU Zhonghua +1 位作者 XIONG Li XU Fangfang 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2015年第2期146-152,共7页
Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mech... Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data (LOD) with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users' requirement greatly. 展开更多
关键词 sentiment analysis microblog keyword extraction linked open data background knowledge base
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