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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ki-Seung Lee JeongSu Kim |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. of Electron. Eng, Konkuk Univ., Seoul, South Korea (Ki-Seung Lee) |
| Abstract | A method for the automatic segmentation of speech signals is described. The method is dedicated to the construction of a large database for a Text-To-Speech (TTS) synthesis system. The main issue of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Markov Model (HMM). Multi-layer perceptron (MLP) was used as a phone boundary detector. To increase the performance of segmentation, a technique which individually trains an MLP according to phonetic transition is proposed. The optimum partitioning of the entire phonetic transition space is constructed from the standpoint of minimizing the overall deviation from hand labelling positions. With single speaker stimuli, the experimental results showed that more than 95% of all phone boundaries have a boundary deviation from the reference position smaller than 20 ms, and the refinement of the boundaries reduces the root mean square error by about 25%. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 291153 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1198765 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech synthesis Databases Hidden Markov models Labeling Automatic speech recognition Signal synthesis Multilayer perceptrons Detectors Root mean square Linear predictive coding |
| Content Type | Text |
| Resource Type | Article |
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