We study the use of morphosyntactic knowledge to process N-best lists. We propose a new score function that combines the parts of speech (POS), language model, and acoustic scores at the sentence level. Experimental results, obtained for French broadcast news transcription, show a significant improvement of the word error rate with various decoding criteria commonly used in speech recognition. Interestingly, we observed more grammatical transcriptions, which translates into a better sentence error rate. Finally, we show that POS knowledge also improves posterior based confidence measures.
Cite as: Huet, S., Gravier, G., Sébillot, P. (2007) Morphosyntactic processing of n-best lists for improved recognition and confidence measure computation. Proc. Interspeech 2007, 1741-1744, doi: 10.21437/Interspeech.2007-488
@inproceedings{huet07_interspeech,
author={Stéphane Huet and Guillaume Gravier and Pascale Sébillot},
title={{Morphosyntactic processing of n-best lists for improved recognition and confidence measure computation}},
year=2007,
booktitle={Proc. Interspeech 2007},
pages={1741--1744},
doi={10.21437/Interspeech.2007-488}
}