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Adaptive language modeling with varied sources to cover new vocabulary items

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3 Author(s)
Schwarm, S.E. ; Dept. of Comput. Sci. & Eng., Univ. of Washington, Seattle, WA, USA ; Bulyko, Ivan ; Ostendorf, M.

N-gram language modeling typically requires large quantities of in-domain training data, i.e., data that matches the task in both topic and style. For conversational speech applications, particularly meeting transcription, obtaining large volumes of speech transcripts is often unrealistic; topics change frequently and collecting conversational-style training data is time-consuming and expensive. In particular, new topics introduce new vocabulary items which are not included in existing models. In this work, we use a variety of data sources (reflecting different sizes and styles), combined using mixture n-gram models. We study the impact of the different sources on vocabulary expansion and recognition accuracy, and investigate possible indicators of the usefulness of a data source. For the task of recognizing meeting speech, we obtain a 9% relative reduction in the overall word error rate and a 61% relative reduction in the word error rate for "new" words added to the vocabulary over a baseline language model trained from general conversational speech data.

Published in:

Speech and Audio Processing, IEEE Transactions on  (Volume:12 ,  Issue: 3 )