Generalizing prosodic prediction of speech recognition errors
Julia Hirschberg, Diane J. Litman, Marc G. J. Swerts · 2000
Since users of spoken dialogue systems have difficulty correcting system misconceptions, it is important for automatic speech recognition (ASR) systems to know when their best hypothesis is incorrect. We compare results of previous experiments which showed that prosody improves the detection of ASR errors to experiments with a new system and new domain, the W99 conference registration system. Our new results again show that prosodic features can improve prediction of ASR misrecognitions over the use of other standard techniques for ASR rejection.