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.
In this paper we present the results obtained when evaluating the Natural Numbers Recognizer of Telefónica I+D over some particular dialects of Spanish from Spain and America. The evaluation was made over two different data sets corresponding to two different situations. A first set includes dialects of Spanish from Spain, that were considered in the training and design of our baseline system, and a second set corresponds to Argentinian Spanish, that was not considered to train the original system. Just because we are interested in a system able to be used by a wide range of users, we tested the possibilities of MAP (Maximum-A-Priori techniques) to adapt the original HMMs in order to represent all the dialects. The experimental results show the capabilities of our recognizer to be used in applications spread over a great number of Spanishspeaking countries.
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