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| Title: | Improving speech recognition by explicit modeling of phone deletions |
| Authors: | Ko, Tom
Mak, Brian Kan-Wing |
| Keywords: | Phone deletions
Acoustic modeling
Fragmented word model
Skip arc
Syllable |
| Issue Date: | Mar-2010 |
| Citation: | Proceedings IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 14-19 March 2010, Dallas, TX, USA, p. 4858-4861. |
| Abstract: | In a paper published by Greenberg in 1998, it was said that in conversational speech, phone deletion rate may go as high as 12% whereas syllable deletion rate is about 1%. The finding prompted a new research direction of syllable modeling for speech recognition. To date, the syllable approach has not yet fulfilled its promise. On the other hand, there were few attempts to model phone deletions explicitly in current ASR systems. In this paper, fragmented word models were derived from well-trained cross-word triphone models, and phone deletion was implemented by skip arcs for words consisting of at least four phonemes. An evaluation on CSR-II WSJ1 Hub2 5K task shows that even with this limited implementation of phone deletions in read speech, we obtained a word error rate reduction of 6.73%. |
| Rights: | © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| URI: | http://hdl.handle.net/1783.1/6561 |
| Appears in Collections: | CSE Conference Papers
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| improving.pdf | | 321Kb | Adobe PDF | View/Open |
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