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Awesome Contextualization of E2E ASR Awesome Contribution

Curated list of awesome papers on Contextualization of E2E ASR.

The purpose of contextualizating ASR outputs is to bias the results towards tokens, generally proper nouns or rare words or jargon, which are thought likely to be produced given the context of an audio signal. Correct transcription of these tokens might have an outsized impact on the value of the output, and incorrect transcription might otherwise be likely.

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Contents

Deep Contextualization

End to end approaches, integrated neural modules

Contextual LAS (CLAS)

Contextual Transducer ("RNNTs")

2021

2022

External Contextualization

External modules such as Language Models, Error Correction models, and weighted FSTs applied to hypotheses of E2E ASR systems

2012

2015

2016

2017

2018

2019

2020

2021

2022