most citedMulti-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2022

Relaxed Attention for Transformer Models

Timo Lohrenz, Björn Möller, Zhengyang Li +1

The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly lear…

eess.AS2021

Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition

Timo Lohrenz, Patrick Schwarz, Zhengyang Li +1

Recently, attention-based encoder-decoder (AED) models have shown high performance for end-to-end automatic speech recognition (ASR) across several tasks. Addressing overconfidence…

eess.AS2021★ 1 cited

Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition

Timo Lohrenz, Zhengyang Li, Tim Fingscheidt

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…