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INFORMATIVE DIALECT RECOGNITION USING CONTEXT-DEPENDENT PRONUNCIATION MODELING

Language Identification

Full Paper at IEEE Xplore

Přednášející: Nancy Chen, Autoři: Nancy Chen, Massachusetts Institute of Technology, United States; Wade Shen, Joseph Campbell, Pedro Torres-Carrasquillo, MIT Lincoln Laboratory, United States

We propose an informative dialect recognition system that learns phonetic transformation rules, and uses them to identify dialects. A hidden Markov model is used to align reference phones with dialect-specific pronunciations to characterize when and how often substitutions, insertions, and deletions occur. Decision tree clustering is used to find context-dependent phonetic rules. We ran recognition tasks on 4 Arabic dialects. Not only do the proposed systems perform well on their own, but when fused with baselines they improve performance by 21-36% relative. In addition, our proposed decision-tree system beats the baseline monophone system in recovering phonetic rules by 21% relative. Pronunciation rules learned by our proposed system quantify the occurrence frequency of known rules, and suggest rule candidates for further linguistic studies.


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  Informace o přednášce

Nahráno: 2011-05-24 10:35 - 10:55, Panorama
Přidáno: 23. 6. 2011 17:40
Počet zhlédnutí: 56
Rozlišení videa: 1024x576 px, 512x288 px
Délka videa: 0:21:00