Model adaptation for sentence segmentation from speech

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% and 8.4% relative error reductions with only 1,000 and 3,000 in-domain sentences, respectively, and 3.7% relative error reduction with all in-domain data of 80,000 words.