activity
20192023
most citedTransformer-Transducer: End-to-End Speech Recognition with Self-Attention

66 citations · 101 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SD2023

Directional Source Separation for Robust Speech Recognition on Smart Glasses

Tiantian Feng, Ju Lin, Yiteng Huang +7

Modern smart glasses leverage advanced audio sensing and machine learning technologies to offer real-time transcribing and captioning services, considerably enriching human experie…

eess.AS20221 cited

SCA: Streaming Cross-attention Alignment for Echo Cancellation

Yang Liu, Yangyang Shi, Yun Li +3

End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art…

eess.AS2019

Spatial Attention for Far-field Speech Recognition with Deep Beamforming Neural Networks

Weipeng He, Lu Lu, Biqiao Zhang +3

In this paper, we introduce spatial attention for refining the information in multi-direction neural beamformer for far-field automatic speech recognition. Previous approaches of n…

cs.CL201934 cited

RNN-T For Latency Controlled ASR With Improved Beam Search

Mahaveer Jain, Kjell Schubert, Jay Mahadeokar +5

Neural transducer-based systems such as RNN Transducers (RNN-T) for automatic speech recognition (ASR) blend the individual components of a traditional hybrid ASR systems (acoustic…

eess.AS201966 cited

Transformer-Transducer: End-to-End Speech Recognition with Self-Attention

Ching-Feng Yeh, Jay Mahadeokar, Kaustubh Kalgaonkar +6

We explore options to use Transformer networks in neural transducer for end-to-end speech recognition. Transformer networks use self-attention for sequence modeling and comes with…