Morphosyntactic Processing of N-Best Lists for Improved Recognition and Confidence Measure Computation

Stéphane Huet 1 Guillaume Gravier 1 Pascale Sébillot 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : 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.
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Stéphane Huet, Guillaume Gravier, Pascale Sébillot. Morphosyntactic Processing of N-Best Lists for Improved Recognition and Confidence Measure Computation. 8th Annual Conference of the International Speech Communication Association (Interspeech), 2007, Antwerp, Belgium. pp.1741-1744. ⟨hal-02021878⟩

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