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Junyoung Chung

Alumni

Publications

Towards Policy-Guided Conversational Recommendation with Dialogue Acts
Paul Crook
Y-Lan Boureau
J. Weston
Akbar Karimi
Leonardo Rossi
Andrea Prati
Wenqiang Lei
Xiangnan He
Qingyun Yisong Miao
Richang Wu
Min-Yen Hong
Kan Tat-Seng
Raymond Li
Samira Ebrahimi Kahou
Hannes Schulz
Zujie Liang
Huang Hu
Can Xu
Jian Miao
Lizi Liao … (voir 47 de plus)
Ryuichi Takanobu
Yunshan Ma
Xun Yang
Wenchang Ma
Minlie Huang
Minghao Tu
Iulian Serban
Alessandro Sordoni
Ryan Lowe
Laurent Charlin
Joelle Pineau
Aaron C. Courville
David Silver
Julian Schrittwieser
K. Simonyan
Ioannis Antonoglou
Aja Huang
A. Guez
Hanlin Zhu
O. Vinyals
Igor Babuschkin
Junyoung Chung
M. Mathieu
Max Jaderberg
Wojciech M. Czar-725 necki
A. Dudzik
Petko Georgiev
Richard Powell
T. Ewalds
Dan Horgan
M. Kroiss
Ivo Danihelka
J. Agapiou
Junhyuk Oh
Valentin Dalibard
David Choi
L. Sifre
Yury Sulsky
Sasha Vezhnevets
James Molloy
Trevor Cai
D. Budden
T. Paine
Caglar Gulçehre
Ziyu Wang
Tobias Pfaff
Tobias Pohlen
2021-12-31
(publié)
www.semanticscholar.org
Dynamic Frame Skipping for Fast Speech Recognition in Recurrent Neural Network Based Acoustic Models
Inchul Song
Junyoung Chung
Taesup Kim
Yoshua Bengio
A recurrent neural network is a powerful tool for modeling sequential data such as text and speech. While recurrent neural networks have ach… (voir plus)ieved record-breaking results in speech recognition, one remaining challenge is their slow processing speed. The main cause comes from the nature of recurrent neural networks that read only one frame at each time step. Therefore, reducing the number of reads is an effective approach to reducing processing time. In this paper, we propose a novel recurrent neural network architecture called Skip-RNN, which dynamically skips speech frames that are less important. The Skip-RNN consists of an acoustic model network and skip-policy network that are jointly trained to classify speech frames and determine how many frames to skip. We evaluate our proposed approach on the Wall Street Journal corpus and show that it can accelerate acoustic model computation by up to 2.4 times without any noticeable degradation in transcription accuracy.
2018-04-14
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org