85 citations · 410 across the 77 of their papers we have counts for
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Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning
Cunhang Fan, Bin Liu, Jianhua Tao +3
Recurrent neural networks (RNNs) have shown significant improvements in recent years for speech enhancement. However, the model complexity and inference time cost of RNNs are much…
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition
Cunhang Fan, Jiangyan Yi, Jianhua Tao +3
The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR). However,…
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition
Shuai Zhang, Jiangyan Yi, Zhengkun Tian +3
Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models'…
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
The RNN-Transducers and improved attention-based encoder-decoder models are widely applied to streaming speech recognition. Compared with these two end-to-end models, the CTC model…
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +3
Non-autoregressive transformer models have achieved extremely fast inference speed and comparable performance with autoregressive sequence-to-sequence models in neural machine tran…
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Although attention based end-to-end models have achieved promising performance in speech recognition, the multi-pass forward computation in beam-search increases inference time cos…