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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Soto, V. Cooper, E. Mangu, L. Rosenberg, A. Hirschberg, J. |
| Copyright Year | 2014 |
| Abstract | We introduce a two-stage cascaded scheme to rescore Confusion Networks (CNs) for Keyword Search in the context of Low-Resource Languages. In the first stage we rescore the CN to improve the error rate of the 1-best hypothesis using a large number of lexical, phonetic, false alarms and structural features. Using a rank learning Support Vector Machine classifier, we obtain WER gains between 0.54% and 2.84% on Cantonese, Tagalog, Turkish, Pashto and Vietnamese. In the second stage we generate keyword hits from the rescored CN and use logistic regression to detect true hits and false alarms. We compare these to hits generated from the unrescored CN and obtain gains between 0.45% and 0.9% on the MTWV metric by using the mentioned features and including acoustic and prosodic features on Tagalog, Turkish and Pashto. |
| Sponsorship | IEEE Signal Process. Soc. |
| Starting Page | 7088 |
| Ending Page | 7092 |
| File Size | 95320 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479928934 |
| DOI | 10.1109/ICASSP.2014.6854975 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-05-04 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Speech Keyword search Feature extraction Acoustics Speech recognition Lattices Standards keyword search error detection error correction confusion networks posting lists rescoring |
| Content Type | Text |
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