Dynamic Combination of Automatic Speech Recognition Systems by Driven DecodingReport as inadecuate

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1 GETALP - Groupe d’Étude en Traduction Automatique-Traitement Automatisé des Langues et de la Parole LIG - Laboratoire d-Informatique de Grenoble 2 LIA - Laboratoire Informatique d-Avignon 3 LIUM - Laboratoire d-Informatique de l-Université du Maine 4 TEXMEX - Multimedia content-based indexing IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique

Abstract : Combining automatic speech recognition ASR systems generally relies on the posterior merging of the outputs or on acoustic cross-adaptation. In this paper, we propose an integrated approach where outputs of secondary systems are integrated in the search algorithm of a primary one. In this driven decoding algorithm DDA, the secondary systems are viewed as observation sources that should be evaluated and combined to others by a primary search algorithm. DDA is evaluated on a subset of the ESTER I corpus consisting of 4 hours of French radio broadcast news. Results demonstrate DDA significantly outperforms vote-based approaches: we obtain an improvement of 14.5% relative word error rate over the best single-systems, as opposed to the the 6.7% with a ROVER combination. An in-depth analysis of the DDA shows its ability to improve robustness gains are greater in adverse conditions and a relatively low dependency on the search algorithm. The application of DDA to both A* and beam-search-based decoder yields similar performances.

Author: Benjamin Lecouteux - Georges Linares - Yannick Estève - Guillaume Gravier -

Source: https://hal.archives-ouvertes.fr/


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