Driving ROVER with Segment-based ASR Quality Estimation

ROVER is a widely used method to combine the output of multiple automatic speech recognition (ASR) systems. Though effective, the basic approach and its variants suffer from potential drawbacks: i) their results depend on the order in which the hypotheses are used to feed the combination process, ii) when applied to combine long hypotheses, they disregard possible differences in transcription quality at local level, iii) they often rely on word confidence information. We address these issues by proposing a segment-based ROVER in which hypothesis ranking is obtained from a confidence-independent ASR quality estimation method. Our results on English data from the IWSLT2012 and IWSLT2013 evaluation campaigns significantly outperform standard ROVER and approximate two strong oracles.