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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weixun Gao Qiying Cao |
| Copyright Year | 2009 |
| Description | Author affiliation: College of Computer Science & Technology, Donghua Univeristy, Shanghai, China (Qiying Cao) || School of Information Science and Technology, Donghua Univeristy, Shanghai, China (Weixun Gao) |
| Abstract | Statistical vocoder based speech synthesis system has a small footprint and a flexibility to change voice characteristics. However, the output synthesized speech sounds mechanic or a little bit buzzy comparing with natural human speech. Mixed excitation model instead of either a periodic impulse train or white noise is commonly used for low bit rate speech coding. In this paper, we extend it to statistical vocoder based speech synthesis. We also compare two methods: comb filter and normalized correlation coefficient, of extracting periodicity ratios for mixed excitation model. Excitation parameters are modeled by HMM in a slave manner, where the state boundaries are given by spectral and pitch models. Two corpora uttered by a male and a female speaker are used to evaluate mixed excitation model. The experimental results show the voice quality of synthesized speech with mixed excitation model can be significantly improved and the method of Comb filter for extracting periodicity ratios slightly outperform normalized correlation coefficient. |
| Starting Page | 193 |
| Ending Page | 196 |
| File Size | 935955 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424446681 |
| DOI | 10.1109/PRIMEASIA.2009.5397413 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-01-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer science Speech coding Vocoders Bit rate Hidden Markov models Humans White noise Educational institutions Speech synthesis Power harmonic filters |
| Content Type | Text |
| Resource Type | Article |
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