Language model parameter estimation using user transcriptions
Author(s)Hsu, Bo-June; Glass, James R.
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In limited data domains, many effective language modeling techniques construct models with parameters to be estimated on an in-domain development set. However, in some domains, no such data exist beyond the unlabeled test corpus. In this work, we explore the iterative use of the recognition hypotheses for unsupervised parameter estimation. We also evaluate the effectiveness of supervised adaptation using varying amounts of user-provided transcripts of utterances selected via multiple strategies. While unsupervised adaptation obtains 80% of the potential error reductions, it is outperformed by using only 300 words of user transcription. By transcribing the lowest confidence utterances first, we further obtain an effective word error rate reduction of 0.6%.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Institute of Electrical and Electronics Engineers
Bo-June Hsu, and J. Glass. “Language model parameter estimation using user transcriptions.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. 2009. 4805-4808. © 2009 IEEE
Final published version
INSPEC Accession Number: 10701485
adaptation, language modeling, speech recognition