Are Morphosyntactic Taggers Suitable to Improve Automatic Transcription?

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 : The aim of our paper is to study the interest of part of speech (POS) tagging to improve speech recognition. We first evaluate the part of misrecognized words that can be corrected using POS information; the analysis of a short extract of French radio broadcast news shows that an absolute decrease of the word error rate by 1.1% can be expected. We also demonstrate quantitatively that traditional POS taggers are reliable when applied to spoken corpus, including automatic transcriptions. This new result enables us to effectively use POS tag knowledge to improve, in a postprocessing stage, the quality of transcriptions, especially correcting agreement errors.
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Stéphane Huet, Guillaume Gravier, Pascale Sébillot. Are Morphosyntactic Taggers Suitable to Improve Automatic Transcription?. 9th International Conference on Text, Speech and Dialogue (TSD), 2006, Brno, Czech Republic. pp.391-398. ⟨hal-02021874⟩

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