5 citations · 6 across the 4 of their papers we have counts for
4 papers
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit
Pengcheng Li, Genshun Wan, Fenglin Ding +4
Speech pre-training has shown great success in learning useful and general latent representations from large-scale unlabeled data. Based on a well-designed self-supervised learning…
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information
Fenglin Ding, Genshun Wan, Pengcheng Li +2
Multilingual end-to-end models have shown great improvement over monolingual systems. With the development of pre-training methods on speech, self-supervised multilingual speech re…
Attentive batch normalization for lstm-based acoustic modeling of speech recognition
Fenglin Ding, Wu Guo, Lirong Dai +1
Batch normalization (BN) is an effective method to accelerate model training and improve the generalization performance of neural networks. In this paper, we propose an improved ba…
Attention-based gated scaling adaptative acoustic model for ctc-based speech recognition
Fenglin Ding, Wu Guo, Lirong Dai +1
In this paper, we propose a novel adaptive technique that uses an attention-based gated scaling (AGS) scheme to improve deep feature learning for connectionist temporal classificat…