Publication Details
Hybrid word-subword decoding for spoken term detection
Fapšo Michal, Ing., Ph.D.
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
spoken term detection
The paper is hybrid word-subword decoding for spoken term detection
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.
@inproceedings{BUT32318,
author="Igor {Szőke} and Michal {Fapšo} and Lukáš {Burget} and Jan {Černocký}",
title="Hybrid word-subword decoding for spoken term detection",
booktitle="Proc. SSCS 2008: Speech search workshop at SIGIR",
year="2008",
pages="1--4",
publisher="Association for Computing Machinery",
address="Singapore",
isbn="978-90-365-2697-5",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2008/szoke_sigir2008.pdf"
}