Generalized driven decoding for speech recognition system combination

TL;DR: Results show that generalized- DDA outperforms significantly ROVER method: it 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.

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.