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BASKAR, M. BURGET, L. WATANABE, S. KARAFIÁT, M. HORI, T. ČERNOCKÝ, J.
Original Title
Promising Accurate Prefix Boosting For Sequence-to-sequence ASR
Type
conference paper
Language
English
Original Abstract
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 and testing scheme effectively. The training procedure involves maximizing the score of each partial correct sequence obtained during beam search compared to other hypotheses. The training objective also includes minimization of token (character) error rate. PAPB shows its efficacy by achieving 10.8% and 3.8% WER with and without external RNNLM respectively on Wall Street Journal dataset.
Keywords
Beam search training, sequence learning, discriminative training, Attention models, softmax-margin
Authors
BASKAR, M.; BURGET, L.; WATANABE, S.; KARAFIÁT, M.; HORI, T.; ČERNOCKÝ, J.
Released
12. 5. 2019
Publisher
IEEE Signal Processing Society
Location
Brighton
ISBN
978-1-5386-4658-8
Book
Proceedings of ICASSP
Pages from
5646
Pages to
5650
Pages count
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" }
Documents
baskar_icassp2019_0005646.pdf