A novel out-of-vocabulary rejection method for isolated word recognition

With the increased use of speech recognition outside of the laboratory environment, the need for better out-of- vocabulary (OOV) rejection techniques is critical for the continued success of this user interface. In this paper, we propose a novel rejection method for isolated word recognition. Unlike the traditional method which is based on the use of garbage models, our proposed method does not need using garbage models explicitly. Therefore it does not need additional memory requirement and there is no increase in computation. So the proposed method is especially suitable for resource limited applications. Results are presented for a database consisting of 33 speakers (13 speakers for training and 20 speakers for testing) and 50 short words (10 in vocabulary words and 40 out of vocabulary words). Performance shows that the proposed method is superior to the traditional rejection method which based on the use of garbage models.