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Detail publikačního výsledku
BASKAR, M.; BURGET, L.; WATANABE, S.; KARAFIÁT, M.; HORI, T.; ČERNOCKÝ, J.
Originální název
Promising Accurate Prefix Boosting For Sequence-to-sequence ASR
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
In this paper, we present promising accurate prefix boosting (PAPB),a discriminative training technique for attention based sequence-tosequence(seq2seq) ASR. PAPB is devised to unify the training andtesting scheme effectively. The training procedure involves maximizingthe score of each partial correct sequence obtained duringbeam search compared to other hypotheses. The training objectivealso includes minimization of token (character) error rate. PAPBshows its efficacy by achieving 10.8% and 3.8% WER with and withoutexternal RNNLM respectively on Wall Street Journal dataset.
Anglický abstrakt
Klíčová slova
Beam search training, sequence learning, discriminativetraining, Attention models, softmax-margin
Klíčová slova v angličtině
Autoři
Rok RIV
2020
Vydáno
12.05.2019
Nakladatel
IEEE Signal Processing Society
Místo
Brighton
ISBN
978-1-5386-4658-8
Kniha
Proceedings of ICASSP
Strany od
5646
Strany do
5650
Strany počet
5
URL
https://ieeexplore.ieee.org/document/8682782
BibTex
@inproceedings{BUT160001, author="BASKAR, M. and BURGET, L. and WATANABE, S. and KARAFIÁT, M. and HORI, T. and ČERNOCKÝ, J.", title="Promising Accurate Prefix Boosting For Sequence-to-sequence ASR", booktitle="Proceedings of ICASSP", year="2019", pages="5646--5650", publisher="IEEE Signal Processing Society", address="Brighton", doi="10.1109/ICASSP.2019.8682782", isbn="978-1-5386-4658-8", url="https://ieeexplore.ieee.org/document/8682782" }
Dokumenty
baskar_icassp2019_0005646