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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rongqing Huang Hansen, J.H.L. |
| Copyright Year | 2007 |
| Description | Author affiliation: Erik Jonsson Sch. of Eng. & Comput. Sci., Texas Univ., Dallas, TX, USA (Rongqing Huang; Hansen, J.H.L.) |
| Abstract | Studies have shown that dialect variation has a significant impact in speech recognition performance, and therefore it is important to be able to perform effective dialect classification to improve speech systems. Dialects differ at the acoustic, grammar, and vocabulary levels. In this study, topic-specific printed text dialect data are collected from the ten major newspapers in Australia, United Kingdom, and United States. An n-gram language model is trained for each topic in each country/dialect. The perplexity measure is applied to classify the dialect-dependent documents. In addition to the n-gram information, further features can be extracted from text structure. Conditional random fields (CRF) is such a model which can extract different levels of features and is still mathematically tractable. The CRF is applied to train the language model and classify documents. Significant improvement on dialect classification is achieved by using the CRF based classifier, especially on the small size documents (10% to 22% relative error reduction). Text classification based on variable size documents is explored and a document with several hundred words is shown to be sufficient for dialect classification. The vocabulary difference among the text documents from different countries are explored and the dialect difference is smoothly connected with the vocabulary difference. Five document topics are evaluated and performance for cross topic dialect classification is explored. |
| File Size | 204556 |
| File Format | |
| ISBN | 1424407273 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2007.367239 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vocabulary Australia Data mining Natural languages Speech recognition Robustness Computer science Acoustic measurements Feature extraction Mathematical model text classification Dialect classification Conditional Random Fields n-gram language model |
| Content Type | Text |
| Resource Type | Article |
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