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.
We are improving a flexible, large vocabulary, speaker independent, isolated-word recognition system in a telephone environment, originally designed as an integrated system doing all the recognition process in one step. We have transformed it, by adopting the hypothesis-verification paradigm.In this paper, we will describe the architecture and results of the hypothesis subsystem. We will show the system evolution and the modifications adopted to face such a difficult task, achieving significant improvements using automatically clustered phonemelike units, semi-continuous HMMs, and multiple models per unit. Also, system behavior for vocabulary dependent and independent tasks and vocabularies up to 10000 words will be tested.
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