Search Results for author: Nina Markl

Found 5 papers, 1 papers with code

Mind the data gap(s): Investigating power in speech and language datasets

no code implementations LTEDI (ACL) 2022 Nina Markl

Algorithmic oppression is an urgent and persistent problem in speech and language technologies.

The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR

no code implementations31 Mar 2023 Ramon Sanabria, Nikolay Bogoychev, Nina Markl, Andrea Carmantini, Ondrej Klejch, Peter Bell

Although the great many advances in English automatic speech recognition (ASR) over the past decades, results are usually reported based on test datasets which fail to represent the diversity of English as spoken today around the globe.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR

no code implementations LREC 2022 Nina Markl, Stephen Joseph McNulty

Despite the fact that variation is a fundamental characteristic of natural language, automatic speech recognition systems perform systematically worse on non-standardised and marginalised language varieties.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Querent Intent in Multi-Sentence Questions

1 code implementation COLING (LAW) 2020 Laurie Burchell, Jie Chi, Tom Hosking, Nina Markl, Bonnie Webber

Multi-sentence questions (MSQs) are sequences of questions connected by relations which, unlike sequences of standalone questions, need to be answered as a unit.

Sentence

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