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Found 3 papers, 2 papers with code
Relaxed Attention for Transformer Models
The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly learned internal language model in the autoregressive transformer decoder complicating the integration of external language models.
Ranked #3 on
Lipreading
on LRS3-TED
(using extra training data)
Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition
Recently, attention-based encoder-decoder (AED) models have shown high performance for end-to-end automatic speech recognition (ASR) across several tasks.
Ranked #7 on
Speech Recognition
on WSJ eval92
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition
Stream fusion, also known as system combination, is a common technique in automatic speech recognition for traditional hybrid hidden Markov model approaches, yet mostly unexplored for modern deep neural network end-to-end model architectures.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
