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%. Index Terms — speech recognition, language modeling, adaptation

We investigate the empirical behavior of n-gram discounts within and across domains. When a language model is trained and evaluated on two corpora from exactly the same domain, discounts are roughly constant, matching the assumptions of modified Kneser-Ney LMs. However, when training and test corpora diverge, the empirical discount grows essentially as a linear function of the n-gram count. We adapt a Kneser-Ney language model to incorporate such growing discounts, resulting in perplexity improvements over modified Kneser-Ney and Jelinek-Mercer baselines. 1