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        <title>How Might We Create Better Benchmarks for Speech Recognition?</title>
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        <namePart type="given">Alëna</namePart>
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    <name type="personal">
        <namePart type="given">Pavel</namePart>
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            <namePart type="given">Kenneth</namePart>
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    <abstract>The applications of automatic speech recognition (ASR) systems are proliferating, in part due to recent significant quality improvements. However, as recent work indicates, even state-of-the-art speech recognition systems – some which deliver impressive benchmark results, struggle to generalize across use cases. We review relevant work, and, hoping to inform future benchmark development, outline a taxonomy of speech recognition use cases, proposed for the next generation of ASR benchmarks. We also survey work on metrics, in addition to the de facto standard Word Error Rate (WER) metric, and we introduce a versatile framework designed to describe interactions between linguistic variation and ASR performance metrics.</abstract>
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%0 Conference Proceedings
%T How Might We Create Better Benchmarks for Speech Recognition?
%A Aksënova, Alëna
%A van Esch, Daan
%A Flynn, James
%A Golik, Pavel
%Y Church, Kenneth
%Y Liberman, Mark
%Y Kordoni, Valia
%S Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F aksenova-etal-2021-might
%X The applications of automatic speech recognition (ASR) systems are proliferating, in part due to recent significant quality improvements. However, as recent work indicates, even state-of-the-art speech recognition systems – some which deliver impressive benchmark results, struggle to generalize across use cases. We review relevant work, and, hoping to inform future benchmark development, outline a taxonomy of speech recognition use cases, proposed for the next generation of ASR benchmarks. We also survey work on metrics, in addition to the de facto standard Word Error Rate (WER) metric, and we introduce a versatile framework designed to describe interactions between linguistic variation and ASR performance metrics.
%R 10.18653/v1/2021.bppf-1.4
%U https://aclanthology.org/2021.bppf-1.4
%U https://doi.org/10.18653/v1/2021.bppf-1.4
%P 22-34

Markdown (Informal)

[How Might We Create Better Benchmarks for Speech Recognition?](https://aclanthology.org/2021.bppf-1.4) (Aksënova et al., BPPF 2021)

• How Might We Create Better Benchmarks for Speech Recognition? (Aksënova et al., BPPF 2021)

ACL

• Alëna Aksënova, Daan van Esch, James Flynn, and Pavel Golik. 2021. How Might We Create Better Benchmarks for Speech Recognition?. In Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future, pages 22–34, Online. Association for Computational Linguistics.