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Acoustics, Speech, and Signal Processing, IEEE International Conference on (2009)
Taipei, Taiwan
Apr. 19, 2009 to Apr. 24, 2009
ISBN: 978-1-4244-2353-8
pp: 4737-4740
Dimitra Vergyri , Speech Technology and Research Laboratory, SRI International, Menlo Park, CA, 94025, USA
Andreas Stolcke , Speech Technology and Research Laboratory, SRI International, Menlo Park, CA, 94025, USA
Gokhan Tur , Speech Technology and Research Laboratory, SRI International, Menlo Park, CA, 94025, USA
ABSTRACT
We investigate languagemodel (LM) adaptation in a meeting recognition application, where the LM is adapted based on recognition output from relevant prior meetings and partial manual corrections. Unlike previous work, which has considered either completely unsupervised or supervised adaptation, we investigate a scenario where a human (e.g., a meeting participant) can correct some of the recognition mistakes. We find that recognition accuracy using the adapted LM can be enhanced substantially by partial correction. In particular, if all content words (about half of all recognition errors) are corrected, recognition improves to the same accuracy as if completely error-free (manually created) transcriptions had been used for adaptation. We also compare and combine a variety of adaptation methods, including linear interpolation, unigram marginal adaptation, and a discriminative method based on “positive” and “negative” N-grams.
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CITATION

D. Vergyri, G. Tur and A. Stolcke, "Exploiting user feedback for language model adaptation in meeting recognition," Acoustics, Speech, and Signal Processing, IEEE International Conference on(ICASSP), Taipei, Taiwan, 2009, pp. 4737-4740.
doi:10.1109/ICASSP.2009.4960689
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