DocumentCode :
2330180
Title :
Unsupervised domain adaptation with multiple acoustic models
Author :
Lei, Xin ; Wang, Wen ; Stolcke, Andreas
Author_Institution :
SRI Int., Menlo Park, CA, USA
fYear :
2010
fDate :
12-15 Dec. 2010
Firstpage :
247
Lastpage :
252
Abstract :
We investigate the problem of adapting a recognition system with multiple acoustic models to a new domain in unsupervised mode. We compare maximum likelihood and discriminative approaches for unsupervised domain adaptation. Different adaptation data selection methods and adaptation strategies are investigated, using a baseline meeting recognition system and adaptation data from a congressional committee web site. Experiments show that one should avoid adapting all acoustic models to the same recognition output, and that ASR confidence estimates improve results when used for rejecting low-quality ASR output. The results show 8% relative overall improvement from unsupervised adaptation.
Keywords :
maximum likelihood estimation; speech recognition; ASR; baseline meeting recognition system; congressional committee Web site; discriminative approaches; maximum likelihood approaches; multiple acoustic models; unsupervised domain adaptation; discriminative adaptation; domain adaptation; unsupervised adaptation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Spoken Language Technology Workshop (SLT), 2010 IEEE
Conference_Location :
Berkeley, CA
Print_ISBN :
978-1-4244-7904-7
Electronic_ISBN :
978-1-4244-7902-3
Type :
conf
DOI :
10.1109/SLT.2010.5700859
Filename :
5700859
Link To Document :
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