9 citations · 25 across the 11 of their papers we have counts for
6 papers · 1 filter
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning
Chen Chen, Yuchen Hu, Qiang Zhang +3
Audio-visual speech recognition (AVSR) has gained remarkable success for ameliorating the noise-robustness of speech recognition. Mainstream methods focus on fusing audio and visua…
Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning
Qiu-Shi Zhu, Long Zhou, Jie Zhang +3
Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (A…
Self-critical Sequence Training for Automatic Speech Recognition
Chen Chen, Yuchen Hu, Nana Hou +3
Although automatic speech recognition (ASR) task has gained remarkable success by sequence-to-sequence models, there are two main mismatches between its training and testing that m…
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning
Chen Chen, Nana Hou, Yuchen Hu +3
Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio. Most prior works usually extract single-modality acou…
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data
Chen Chen, Nana Hou, Yuchen Hu +2
Noise-robust speech recognition systems require large amounts of training data including noisy speech data and corresponding transcripts to achieve state-of-the-art performances in…
Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition
Yuchen Hu, Nana Hou, Chen Chen +1
Automatic speech recognition (ASR) systems degrade significantly under noisy conditions. Recently, speech enhancement (SE) is introduced as front-end to reduce noise for ASR, but i…