18 citations · 32 across the 2 of their papers we have counts for
7 papers
On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
Jinyu Li, Yu Wu, Yashesh Gaur +3
Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E method…
Curriculum Pre-training for End-to-End Speech Translation
Chengyi Wang, Yu Wu, Shujie Liu +2
End-to-end speech translation poses a heavy burden on the encoder, because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful…
Low Latency End-to-End Streaming Speech Recognition with a Scout Network
Chengyi Wang, Yu Wu, Shujie Liu +4
The attention-based Transformer model has achieved promising results for speech recognition (SR) in the offline mode. However, in the streaming mode, the Transformer model usually…
Semantic Mask for Transformer based End-to-End Speech Recognition
Chengyi Wang, Yu Wu, Yujiao Du +7
Attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. This approach takes advantage of t…
Accelerating Transformer Decoding via a Hybrid of Self-attention and Recurrent Neural Network
Chengyi Wang, Shuangzhi Wu, Shujie Liu
Due to the highly parallelizable architecture, Transformer is faster to train than RNN-based models and popularly used in machine translation tasks. However, at inference time, eac…
Source Dependency-Aware Transformer with Supervised Self-Attention
Chengyi Wang, Shuangzhi Wu, Shujie Liu
Recently, Transformer has achieved the state-of-the-art performance on many machine translation tasks. However, without syntax knowledge explicitly considered in the encoder, incor…