Exploiting user feedback for language model adaptation in meeting recognition

Dimitra Vergyri, Andreas Stolcke, Gokhan Tur
2009 2009 IEEE International Conference on Acoustics, Speech and Signal Processing  
We investigate language model (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
more » ... ced 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.
doi:10.1109/icassp.2009.4960689 dblp:conf/icassp/VergyriST09 fatcat:u3y6fz55nrb7rihssyinnmhjsi