<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kulkarni-etal-2024-balancing">
    <titleInfo>
        <title>The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ajinkya</namePart>
        <namePart type="family">Kulkarni</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Tokareva</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rameez</namePart>
        <namePart type="family">Qureshi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Miguel</namePart>
        <namePart type="family">Couceiro</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="family">B</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Buitelaar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thenmozhi</namePart>
            <namePart type="family">Durairaj</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">György</namePart>
            <namePart type="family">Kovács</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Miguel</namePart>
            <namePart type="given">Ángel</namePart>
            <namePart type="family">García Cumbreras</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">St. Julian’s, Malta</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances. This study is dedicated to a comprehensive exploration of the Whisper and MMS systems, with a focus on assessing biases in automatic speech recognition (ASR) inherent to casual conversation speech specific to the Portuguese language. Our investigation encompasses various categories, including gender, age, skin tone color, and geo-location. Alongside traditional ASR evaluation metrics such as Word Error Rate (WER), we have incorporated p-value statistical significance for gender bias analysis. Furthermore, we extensively examine the impact of data distribution and empirically show that oversampling techniques alleviate such stereotypical biases. This research represents a pioneering effort in quantifying biases in the Portuguese language context through the application of MMS and Whisper, contributing to a better understanding of ASR systems’ performance in multilingual settings.</abstract>
    <identifier type="citekey">kulkarni-etal-2024-balancing</identifier>
    <location>
        <url>https://aclanthology.org/2024.ltedi-1.4</url>
    </location>
    <part>
        <date>2024-03</date>
        <extent unit="page">
            <start>31</start>
            <end>40</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese
%A Kulkarni, Ajinkya
%A Tokareva, Anna
%A Qureshi, Rameez
%A Couceiro, Miguel
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F kulkarni-etal-2024-balancing
%X In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances. This study is dedicated to a comprehensive exploration of the Whisper and MMS systems, with a focus on assessing biases in automatic speech recognition (ASR) inherent to casual conversation speech specific to the Portuguese language. Our investigation encompasses various categories, including gender, age, skin tone color, and geo-location. Alongside traditional ASR evaluation metrics such as Word Error Rate (WER), we have incorporated p-value statistical significance for gender bias analysis. Furthermore, we extensively examine the impact of data distribution and empirically show that oversampling techniques alleviate such stereotypical biases. This research represents a pioneering effort in quantifying biases in the Portuguese language context through the application of MMS and Whisper, contributing to a better understanding of ASR systems’ performance in multilingual settings.
%U https://aclanthology.org/2024.ltedi-1.4
%P 31-40

Markdown (Informal)

[The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese](https://aclanthology.org/2024.ltedi-1.4) (Kulkarni et al., LTEDI-WS 2024)

• The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese (Kulkarni et al., LTEDI-WS 2024)

ACL

• Ajinkya Kulkarni, Anna Tokareva, Rameez Qureshi, and Miguel Couceiro. 2024. The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 31–40, St. Julian's, Malta. Association for Computational Linguistics.