2 citations · 2 across the 4 of their papers we have counts for
4 papers
Robust Audio Tagging under Class-wise Supervision Unreliability
Yuanbo Hou, Zhaoyi Liu, Tong Ye +4
Weakly labeled datasets such as AudioSet have driven recent progress in audio tagging. However, annotation quality varies across sound classes. Labels may be incomplete, ambiguous,…
Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition
Haoyu Tang, Zhaoyi Liu, Chang Zeng +1
Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR…
Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition
Zezhong Jin, Dading Zhong, Xiao Song +3
Fine tuning self supervised pretrained models using pseudo labels can effectively improve speech recognition performance. But, low quality pseudo labels can misguide decision bound…
CT-SAT: Contextual Transformer for Sequential Audio Tagging
Yuanbo Hou, Zhaoyi Liu, Bo Kang +2
Sequential audio event tagging can provide not only the type information of audio events, but also the order information between events and the number of events that occur in an au…