ASR applications like nationwide telephone directory assistance (DA) face the challenge of making a correct classification with only minimal amounts of acoustic data. For this reason, current systems still make too many errors in order to be useful. In the perspective of the idea that no recognition is better than misrecognition, a feasible system should therefore detect and reject the least reliable hypotheses. This process is known as utterance verification.
Against the disadvantage of having few information, there is the advantage that isolated utterances have a relatively small degree of prosodic variation, for instance in intonation, speech rate and accent. In this paper we investigate how one can capitalise on this advantage in terms of better utterance verification. We define a number of confidence measures (CMs) on prosodic features and evaluate several linear combinations of one or more CMs.
Experimental results on a field corpus of city names show that a relative improvement of 11.0% Confidence Error Rate can be achieved when compared to a conventional system with only a Log Likelihood Ratio CM.
Cite as: Bouwman, G., Boves, L. (2001) Using information on lexical stress for utterance verification. Proc. ITRW on Prosody in Speech Recognition and Understanding, paper 5
@inproceedings{bouwman01_prosody,
author={Gies Bouwman and Lou Boves},
title={{Using information on lexical stress for utterance verification}},
year=2001,
booktitle={Proc. ITRW on Prosody in Speech Recognition and Understanding},
pages={paper 5}
}