Abstract
This paper analyzes various methods to adapt sentence segmentation models trained on conversational telephone speech (CTS) to meeting style conversations. The sentence segmentation model trained using a large amount of CTS data is used to improve the performance when various amounts of meeting data are available. We test the sentence segmentation performance on both reference and speech-to-text (STT) conditions on the ICSI MRDA Meeting Corpus using the Switchboard CTS Corpus as the out-of-domain data. Results show that the sentence segmentation performance is significantly improved by the adapted classification model compared to the one obtained by using in-domain data only, independently of the amount of in-domain data used: 17.5 pct. and 8.4 pct. relative error reductions with only 1,000 and 3,000 in-domain sentences, respectively, and 3.7 pct. relative error reduction with all in-domain data of 80,000 words.
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