Model Adaptation for Sentence Segmentation from Speech


S. Cuendet, D. Hakkani-Tur and G. Tur, “Model adaptation for sentence segmentation from speech,” 2006 IEEE Spoken Language Technology Workshop, 2006, pp. 102-105, doi: 10.1109/SLT.2006.326827.


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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