On-line garbage modeling with discriminant analysis for utterance verification

Abstract

Out-of-vocabulary (OOV) utterance detection and rejection are specially important and difficult problems in large-vocabulary and continuous speech recognition. In [1] we proposed an utterance verification procedure based on the use of frame-by-frame best acoustic state scores instead of using explicit garbage models. This procedure is usually referred to as on-line garbage modeling.In this contribution we extend our previous work in two major directions: a) we analyze, through the use of Discriminant Analysis, the possibilities of using L-best local scores and N-best utterance hypotheses scores for utterance verification; b) we present experimental results not only for a spontaneously spoken natural number recognition task, as in [1], but also for a flexible large vocabulary recognition task. All the results, based on a telephone database, show that the proposed on-line garbage modeling procedure outperforms, both in performance and computational cost, to other approaches based on the use of explicit garbage models.