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        <title>Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool</title>
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    <name type="personal">
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            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Samuel</namePart>
            <namePart type="family">Louvan</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Andrea</namePart>
            <namePart type="family">Madotto</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Brielen</namePart>
            <namePart type="family">Madureira</namePart>
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    <abstract>Automatic speech recognition (ASR) has evolved from a pipeline architecture with pronunciation dictionaries, phonetic features and language models to the end-to-end systems performing a direct translation from a raw waveform into a word sequence. With the increase in accuracy and the availability of pre-trained models, the ASR systems are now omnipresent in our daily applications. On the other hand, the models’ interpretability and their computational cost have become more challenging, particularly when dealing with less-common languages or identifying regional variations of speakers. This research proposal will follow a four-stage process: 1) Proving an overview of acoustic features and feature extraction algorithms; 2) Exploring current ASR models, tools, and performance assessment techniques; 3) Aligning features with interpretable phonetic transcripts; and 4) Designing a prototype ARPOCA to increase awareness of regional language variation and improve models feedback by developing a semi-automatic acoustic features extraction using PRAAT in conjunction with phonetic transcription.</abstract>
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%0 Conference Proceedings
%T Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool
%A Merz, Megan
%A Scrivner, Olga
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F merz-scrivner-2022-discourse
%X Automatic speech recognition (ASR) has evolved from a pipeline architecture with pronunciation dictionaries, phonetic features and language models to the end-to-end systems performing a direct translation from a raw waveform into a word sequence. With the increase in accuracy and the availability of pre-trained models, the ASR systems are now omnipresent in our daily applications. On the other hand, the models’ interpretability and their computational cost have become more challenging, particularly when dealing with less-common languages or identifying regional variations of speakers. This research proposal will follow a four-stage process: 1) Proving an overview of acoustic features and feature extraction algorithms; 2) Exploring current ASR models, tools, and performance assessment techniques; 3) Aligning features with interpretable phonetic transcripts; and 4) Designing a prototype ARPOCA to increase awareness of regional language variation and improve models feedback by developing a semi-automatic acoustic features extraction using PRAAT in conjunction with phonetic transcription.
%R 10.18653/v1/2022.acl-srw.28
%U https://aclanthology.org/2022.acl-srw.28
%U https://doi.org/10.18653/v1/2022.acl-srw.28
%P 366-372

Markdown (Informal)

[Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool](https://aclanthology.org/2022.acl-srw.28) (Merz & Scrivner, ACL 2022)

• Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool (Merz & Scrivner, ACL 2022)

ACL

• Megan Merz and Olga Scrivner. 2022. Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 366–372, Dublin, Ireland. Association for Computational Linguistics.