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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shifeng Pan Nankaku, Y. Tokuda, K. Jianhua Tao |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computer Science, Nagoya Institute of Technology, Japan (Nankaku, Y.; Tokuda, K.) || National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China (Shifeng Pan; Jianhua Tao) |
| Abstract | The speech parameter generation algorithm considering global variance (GV) for HMM-based speech synthesis proved to be effective against the over-smoothing problem. However, the correlation between dimensions of parameter vector is not sufficiently considered in the current GV model. For some parameters, e.g., Line Spectral Pairs (LSP), the difference of adjacent LSPs has strong influence on the spectral envelope. Considering this important feature, the paper proposes a GV modeling on the difference of adjacent LSPs, i.e., GV on frequency domain delta LSP. By improving the GV likelihood on frequency domain delta LSP, the over-smoothing effect of generated parameter trajectory is better alleviated than conventional one. The result of a perceptual evaluation shows the proposed method outperforms the conventional one, and the naturalness of synthetic speech is improved. |
| Starting Page | 4716 |
| Ending Page | 4719 |
| File Size | 341900 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457705380 |
| ISSN | 15206149 |
| e-ISBN | 9781457705397 |
| e-ISBN | 9781457705373 |
| DOI | 10.1109/ICASSP.2011.5947408 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-22 |
| Publisher Place | Czech Republic |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Speech Frequency domain analysis Speech synthesis Correlation Training Trajectory global variance speech synthesis hidden Markov model |
| Content Type | Text |
| Resource Type | Article |
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