Dynamic Combination of Automatic Speech Recognition Systems by Driven Decoding

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.
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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, Institute of Electrical and Electronics Engineers, 2013. ⟨hal-00758626⟩

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