Name
Abstract | ||
|---|---|---|
This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the word level rather than making a single decision at the utterance level. The basic WDC algorithm also provides options for further modeling and decision strategy improvement. Two sets of classifiers are employed for WDC: a word classifier DW(k) and an utterance classifier D u. DW(k) is boosted via the AdaBoost algorithm directly in the probability space instead of the traditional feature space. Du is boosted via the dialect dependency information of the words. For a small training corpus, it is difficult to obtain a robust statistical model for each word and each dialect. Therefore, a context adapted training (CAT) algorithm is formulated, which adapts the universal phoneme Gaussian mixture models (GMMs) to dialect-dependent word hidden Markov models (HMMs) via linear regression. Three separate dialect corpora are used in the evaluations that include the Wall Street Journal (American and British English), NATO N4 (British, Canadian, Dutch, and German accent English), and IViE (eight British dialects). Significant improvement in dialect classification is achieved for all corpora tested |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TASL.2006.881695 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
accent classification,linear regression,multiple combination decision,speech recognition,large vocabulary continuous speech recognition,decision strategy improvement,dialect-dependent word hidden markov models,regression analysis,word-based modeling,accent/dialect classification,dialect dependency information,context adapted training algorithm,english,context adapted trianing,basic wdc algorithm,text-independent decision problem,dialect classification,decision strategy,limited training data,unrestricted audio,gaussian processes,robust acoustic modeling,word-based dialect classification,gaussian mixture models,word-based modeling technique,english dialect,british dialect,natural language processing,adaboost algorithm,new algorithm,hidden markov models,separate dialect corpus,decision problem,robust statistics,gaussian mixture model,hidden markov model,feature space | Speech processing,Computer science,Artificial intelligence,Natural language processing,Classifier (linguistics),British English,Feature vector,Pattern recognition,Speech recognition,Hidden Markov model,Vocabulary,Mixture model,Computational complexity theory | Journal |
Volume | Issue | ISSN |
15 | 2 | 1558-7916 |
Citations | PageRank | References |
12 | 0.69 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Huang | 1 | 12 | 0.69 |
| John H. L. Hansen | 2 | 3215 | 365.75 |
| Pongtep Angkititrakul | 3 | 179 | 15.47 |