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Generalized Driven Decoding for Speech Recognition System Combination

Abstract : Driven Decoding Algorithm (DDA) is initially an integrated approach for the combination of 2 speech recognition (ASR) systems. It consists in guiding the search algorithm of a primary ASR system by the one-best hypothesis of an auxiliary system. In this paper , we generalize DDA to confusion-network driven decoding and we propose new combination schemes for multiple system combination. Since previous experiments involved 2 ASR systems on broadcast news data, the proposed extended DDA is evaluated using 3 ASR systems from different labs. Results show that generalized-DDA outperforms significantly ROVER method: we obtain a 15.7% relative word error rate improvement with respect to the best single system, as opposed to 8.5% with the ROVER combination.
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Benjamin Lecouteux, Georges Linarès, Yannick Estève, Guillaume Gravier. Generalized Driven Decoding for Speech Recognition System Combination. IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2008, Las Vegas, United States. ⟨hal-01318069⟩

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