80 citations · 80 across the 3 of their papers we have counts for
3 papers
eess.AS2019
Teach an all-rounder with experts in different domains
Zhao You, Dan Su, Dong Yu
In many automatic speech recognition (ASR) tasks, an ideal model has to be applicable over multiple domains. In this paper, we propose to teach an all-rounder with experts in diffe…
cs.SD2019★ 80 cited
End-to-End Multi-Channel Speech Separation
Rongzhi Gu, Jian Wu, Shi-Xiong Zhang +6
The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new end…
cs.LG2019
Learning discriminative features in sequence training without requiring framewise labelled data
Jun Wang, Dan Su, Jie Chen +4
In this work, we try to answer two questions: Can deeply learned features with discriminative power benefit an ASR system's robustness to acoustic variability? And how to learn the…