36 citations · 109 across the 17 of their papers we have counts for
6 papers · 2 filters
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning
Qiushi Zhu, Long Zhou, Ziqiang Zhang +7
Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, t…
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…
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization
Xiao-Ying Zhao, Qiu-Shi Zhu, Jie Zhang
With the development of deep learning, neural network-based speech enhancement (SE) models have shown excellent performance. Meanwhile, it was shown that the development of self-su…
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR
Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +1
Speech enhancement (SE) is usually required as a front end to improve the speech quality in noisy environments, while the enhanced speech might not be optimal for automatic speech…
Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
Xing-Yu Chen, Qiu-Shi Zhu, Jie Zhang +1
In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. By using the acoustic signals t…
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition
Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +3
Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.…