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
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
基金supported by the National Natural Science Foundation of China(61071215)the Science and Technology Foundation of Suzhou(SYG201033)the Pre-research Foundation of Soochow University(Q311901111,14317399)
文摘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.
基金Supported by the National Natural Science Foundation of China(60803160 and 61272110)the Key Projects of National Social Science Foundation of China(11&ZD189)+4 种基金the Natural Science Foundation of Hubei Province(2013CFB334)the Natural Science Foundation of Educational Agency of Hubei Province(Q20101110)the State Key Lab of Software Engineering Open Foundation of Wuhan University(SKLSE2012-09-07)the Teaching Research Project of Hubei Province(2011s005)the Wuhan Key Technology Support Program(2013010602010216)
文摘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.