Lattice-based lexical cues for word fragment detection in conversational speech

Previous approaches to the problem of word fragment detection in speech have focussed primarily on acoustic-prosodic features. This paper proposes that the output of a continuous automatic speech recognition (ASR) system can also be used to derive robust lexical features for the task. We hypothesize that the confusion in the word lattice generated by the ASR system can be exploited for detecting word fragments. Two sets of lexical features are proposed -one which is based on the word confusion, and the other based on the pronunciation confusion between the word hypotheses in the lattice. Classification experiments with a support vector machine (SVM) classifier show that these lexical features perform better than the previously proposed acoustic-prosodic features by around 5.20% (relative) on a corpus chosen from the DARPA Transtac Iraqi-English (San Diego) corpus. A combination of both these feature sets improves the word fragment detection accuracy by 11.50% relative to using just the acoustic-prosodic features.