Dynamic Combination of Automatic Speech Recognition Systems by Driven Decoding

Benjamin Lecouteux (1) , Georges Linares (2) , Yannick Estève (3) , Guillaume Gravier (4)

1 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole
2 LIA - Laboratoire Informatique d'Avignon
3 LIUM - Laboratoire d'Informatique de l'Université du Maine
4 TEXMEX - Multimedia content-based indexing

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.

Domaines: Multimédia [cs.MM]

Benjamin Lecouteux, Georges Linares, Yannick Estève, Guillaume Gravier. Dynamic Combination of Automatic Speech Recognition Systems by Driven Decoding. IEEE Transactions on Audio, Speech and Language Processing, 2013. ⟨hal-00758626⟩