85 citations · 119 across the 10 of their papers we have counts for
13 papers
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
Transducer-based models, such as RNN-Transducer and transformer-transducer, have achieved great success in speech recognition. A typical transducer model decodes the output sequenc…
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because the decoder predicts text tokens (such as characters or wor…
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…