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@inproceedings{1405237,
author = {Svec, Jan and Psutka, Josef V. and Trmal, Jan and Smidl, Lubos and Ircing, Pavel and Sedmidubský, Jan},
address = {Neuveden},
booktitle = {43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)},
doi = {http://dx.doi.org/10.1109/ICASSP.2018.8461774},
keywords = {spoken term detection; speech indexing; grapheme-based speech recognition; keyword search},
howpublished = {paměťový nosič},
language = {eng},
location = {Neuveden},
isbn = {978-1-5386-4658-8},
pages = {6259-6263},
publisher = {IEEE Computer Society},
title = {ON THE USE OF GRAPHEME MODELS FOR SEARCHING IN LARGE SPOKEN ARCHIVES},
year = {2018}
}
TY - JOUR
ID - 1405237
AU - Svec, Jan - Psutka, Josef V. - Trmal, Jan - Smidl, Lubos - Ircing, Pavel - Sedmidubský, Jan
PY - 2018
TI - ON THE USE OF GRAPHEME MODELS FOR SEARCHING IN LARGE SPOKEN ARCHIVES
PB - IEEE Computer Society
CY - Neuveden
SN - 9781538646588
KW - spoken term detection
KW - speech indexing
KW - grapheme-based speech recognition
KW - keyword search
N2 - This paper explores the possibility to use grapheme-based word and sub-word models in the task of spoken term detection (STD). The usage of grapheme models eliminates the need for expert-prepared pronunciation lexicons (which are often far from complete) and/or trainable grapheme-to-phoneme (G2P) algorithms that are frequently rather inaccurate, especially for rare words (words coming from a different language). Moreover, the G2P conversion of the search terms that need to be performed on-line can substantially increase the response time of the STD system. Our results show that using various grapheme-based models, we can achieve STD performance (measured in terms of ATWV) comparable with phoneme-based models but without the additional burden of G2P conversion.
ER -
SVEC, Jan, Josef V. PSUTKA, Jan TRMAL, Lubos SMIDL, Pavel IRCING and Jan SEDMIDUBSKÝ. ON THE USE OF GRAPHEME MODELS FOR SEARCHING IN LARGE SPOKEN ARCHIVES. In \textit{43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)}. Neuveden: IEEE Computer Society, 2018, p.~6259-6263. ISBN~978-1-5386-4658-8. Available from: https://dx.doi.org/10.1109/ICASSP.2018.8461774.
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