Enhanced Out of Vocabulary Word Detection Using Local Acoustic Information

The detection of Out-of-vocabulary (OOV) words is a crucial problem for spoken term detection (STD). In this paper, the use of integration with local acoustic information is investigated to retrieve more OOV words. Tokens with high local acoustic probabilities propagated in the search space at the decoding stage will be forced to propagate to the next frame. In this way, acoustic similar words can be reserved in recognition results without considering of language model probabilities. Experimental results show that this new approach results in a significant increase in the performance of OOV words detection. At least a relative improvement of 8.5% in equal error rate is achieved over the baseline system. Meanwhile, it will do no harm to in-vocabulary (IV) words detection. With some refinement of beam pruning, the decoding time only rises 3% relative to the baseline system.