AI summary

This paper introduces a hybrid word-subword recognition system for spoken term detection, utilizing a hybrid recognition network and hybrid word-subword lattices. Evaluated on spoken term detection accuracy and index size, the multigram model trained on the word recognizer vocabulary shows improvement in word recognition accuracy.

Abstract

This paper deals with a hybrid word-subword recognition system for spoken term detection. The decoding is driven by a hybrid recognition network and the decoder directly produces hybrid word-subword lattices. One phone and two multigram models were tested to represent sub-word units. The systems were evaluated in terms of spoken term detection accuracy and the size of index. We concluded that the best subword model for hybrid word-subword recognition is the multigram model trained on the word recognizer vocabulary. We achieved an improvement in word recognition accuracy, and in spoken term detection accuracy when in-vocabulary and out-of-vocabulary terms are searched separately. Spoken term detection accuracy with the full (in-vocabulary and out-of-vocabulary) term set was slightly worse but the required index size was significantly reduced.