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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chelba, C. Mahajan, M. Acero, A. |
| Copyright Year | 2003 |
| Description | Author affiliation: Microsoft Corp., Redmond, WA, USA (Chelba, C.; Mahajan, M.; Acero, A.) |
| Abstract | The paper presents a series of experiments on speech utterance classification performed on the ATIS corpus. We compare the performance of n-gram classifiers with that of Naive Bayes and maximum entropy classifiers. The n-gram classifiers have the advantage that one can use a single pass system (concurrent speech recognition and classification) whereas for Naive Bayes or maximum entropy classification we use a two-stage system: speech recognition followed by classification. Substantial relative improvements (up to 55%) in classification accuracy can be obtained using discriminative. training methods that belong to the class of conditional maximum likelihood techniques. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 283594 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1198772 |
| 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 | Niobium Entropy Text categorization Speech recognition Vocabulary Testing Natural languages User interfaces Routing Error analysis |
| Content Type | Text |
| Resource Type | Article |
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