Three—layer back—propagation neural networks were used for processing the information provided by the ultraviolet spectra of five—component mixtures, affecting the simultaneous determination of the components with ...Three—layer back—propagation neural networks were used for processing the information provided by the ultraviolet spectra of five—component mixtures, affecting the simultaneous determination of the components with satisfactory results.展开更多
A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech ar...A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech are extracted, and mapped to the eigenspace which is formed by eigenvectors of its scatter matrix, thereby the Structured Gaussian Mixture Model in the EigenSpace （SGMM-ES） is trained. The source and target speaker＇s SGMM-ES are matched based on Acoustic Universal Structure （AUS） principle to achieve spectrum transform function. Experimental results show the speaker identification rate of conversion speech achieves 95.25%, and the value of average cepstrum distortion is 1.25 which is 0.8% and 7.3% higher than the performance of SGMM method respectively. ABX and MOS evaluations indicate the conversion performance is quite close to the traditional method under the parallel corpora condition. The results show the eigenspace based structured Gaussian mixture model for voice conversion under the non-parallel corpora is effective.展开更多
基金Project supported by National Natural Science Foundatlon of China.
文摘Three—layer back—propagation neural networks were used for processing the information provided by the ultraviolet spectra of five—component mixtures, affecting the simultaneous determination of the components with satisfactory results.
基金supported by the Natural Science Foundation of China(61271360)the Application Fundamental Research Project of Suzhou(SYG201230)
文摘A new methodology of voice conversion in cepstrum eigenspace based on structured Gaussian mixture model is proposed for non-parallel corpora without joint training. For each speaker, the cepstrum features of speech are extracted, and mapped to the eigenspace which is formed by eigenvectors of its scatter matrix, thereby the Structured Gaussian Mixture Model in the EigenSpace （SGMM-ES） is trained. The source and target speaker＇s SGMM-ES are matched based on Acoustic Universal Structure （AUS） principle to achieve spectrum transform function. Experimental results show the speaker identification rate of conversion speech achieves 95.25%, and the value of average cepstrum distortion is 1.25 which is 0.8% and 7.3% higher than the performance of SGMM method respectively. ABX and MOS evaluations indicate the conversion performance is quite close to the traditional method under the parallel corpora condition. The results show the eigenspace based structured Gaussian mixture model for voice conversion under the non-parallel corpora is effective.
