Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion
The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal...
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| Format: | Article |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2013-03-01
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| Series: | Archives of Acoustics |
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| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/5 |
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| author | Peng SONG Li ZHAO Yongqiang BAO |
| author_facet | Peng SONG Li ZHAO Yongqiang BAO |
| author_sort | Peng SONG |
| collection | DOAJ |
| description | The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality. |
| format | Article |
| id | doaj-art-e74a284ba7824012a94c7c7bf48a3fd5 |
| institution | DOAJ |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2013-03-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-e74a284ba7824012a94c7c7bf48a3fd52025-08-20T02:39:23ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2013-03-01381Spectral Mapping Using Kernel Principal Components Regression for Voice ConversionPeng SONG0Li ZHAO1Yongqiang BAO2Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast UniversityKey Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast UniversitySchool of Communication Engineering, Nanjing Institute of TechnologyThe Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality.https://acoustics.ippt.pan.pl/index.php/aa/article/view/5spectral mappingoverfittingoversmoothingdiscontinuitykernel principal component regression |
| spellingShingle | Peng SONG Li ZHAO Yongqiang BAO Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion Archives of Acoustics spectral mapping overfitting oversmoothing discontinuity kernel principal component regression |
| title | Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion |
| title_full | Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion |
| title_fullStr | Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion |
| title_full_unstemmed | Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion |
| title_short | Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion |
| title_sort | spectral mapping using kernel principal components regression for voice conversion |
| topic | spectral mapping overfitting oversmoothing discontinuity kernel principal component regression |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/5 |
| work_keys_str_mv | AT pengsong spectralmappingusingkernelprincipalcomponentsregressionforvoiceconversion AT lizhao spectralmappingusingkernelprincipalcomponentsregressionforvoiceconversion AT yongqiangbao spectralmappingusingkernelprincipalcomponentsregressionforvoiceconversion |