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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dufour, R. Esteve, Y. |
| Copyright Year | 2008 |
| Description | Author affiliation: LIUM, Univ. du Maine, Le Mans (Dufour, R.; Esteve, Y.) |
| Abstract | Automatic speech recognition (ASR) systems are used in a large number of applications, in spite of the inevitable recognition errors. In this study we propose a pragmatic approach to automatically repair ASR outputs by taking into account linguistic and acoustic information, using formal rules or stochastic methods. The proposed strategy consists in developing a specific correction solution for each specific kind of errors. In this paper, we apply this strategy on two case studies specific to French language. We show that it is possible, on automatic transcriptions of French broadcast news, to decrease the error rate of a specific error by 11.4% in one of two the case studies, and 86.4% in the other one. These results are encouraging and show the interest of developing more specific solutions to cover a wider set of errors in a future work. |
| Starting Page | 213 |
| Ending Page | 216 |
| File Size | 89938 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424434718 |
| DOI | 10.1109/SLT.2008.4777878 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-15 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Error analysis Natural languages Stochastic processes language modeling Information retrieval homophones Automatic speech recognition Feedback Broadcasting Error correction Man machine systems Indexing error correction |
| Content Type | Text |
| Resource Type | Article |
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