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%.
Hsu et al. REF explored the iterative use of the ASR hypotheses for unsupervised parameter estimation for n-gram language models.
Language model parameter estimation using user transcriptions
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Computer Science