<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="aggazzotti-etal-2024-authorship">
    <titleInfo>
        <title>Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Cristina</namePart>
        <namePart type="family">Aggazzotti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nicholas</namePart>
        <namePart type="family">Andrews</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Elizabeth</namePart>
        <namePart type="given">Allyn</namePart>
        <namePart type="family">Smith</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Authorship verification is the task of determining if two distinct writing samples share the same author and is typically concerned with the attribution of written text. In this paper, we explore the attribution of transcribed speech, which poses novel challenges. The main challenge is that many stylistic features, such as punctuation and capitalization, are not informative in this setting. On the other hand, transcribed speech exhibits other patterns, such as filler words and backchannels (e.g., um, uh-huh), which may be characteristic of different speakers. We propose a new benchmark for speaker attribution focused on human-transcribed conversational speech transcripts. To limit spurious associations of speakers with topic, we employ both conversation prompts and speakers participating in the same conversation to construct verification trials of varying difficulties. We establish the state of the art on this new benchmark by comparing a suite of neural and non-neural baselines, finding that although written text attribution models achieve surprisingly good performance in certain settings, they perform markedly worse as conversational topic is increasingly controlled. We present analyses of the impact of transcription style on performance as well as the ability of fine-tuning on speech transcripts to improve performance.1</abstract>
    <identifier type="citekey">aggazzotti-etal-2024-authorship</identifier>
    <identifier type="doi">10.1162/tacl_a_00678</identifier>
    <location>
        <url>https://aclanthology.org/2024.tacl-1.49</url>
    </location>
    <part>
        <date>2024</date>
        <detail type="volume"><number>12</number></detail>
        <extent unit="page">
            <start>875</start>
            <end>891</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?
%A Aggazzotti, Cristina
%A Andrews, Nicholas
%A Smith, Elizabeth Allyn
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F aggazzotti-etal-2024-authorship
%X Authorship verification is the task of determining if two distinct writing samples share the same author and is typically concerned with the attribution of written text. In this paper, we explore the attribution of transcribed speech, which poses novel challenges. The main challenge is that many stylistic features, such as punctuation and capitalization, are not informative in this setting. On the other hand, transcribed speech exhibits other patterns, such as filler words and backchannels (e.g., um, uh-huh), which may be characteristic of different speakers. We propose a new benchmark for speaker attribution focused on human-transcribed conversational speech transcripts. To limit spurious associations of speakers with topic, we employ both conversation prompts and speakers participating in the same conversation to construct verification trials of varying difficulties. We establish the state of the art on this new benchmark by comparing a suite of neural and non-neural baselines, finding that although written text attribution models achieve surprisingly good performance in certain settings, they perform markedly worse as conversational topic is increasingly controlled. We present analyses of the impact of transcription style on performance as well as the ability of fine-tuning on speech transcripts to improve performance.1
%R 10.1162/tacl_a_00678
%U https://aclanthology.org/2024.tacl-1.49
%U https://doi.org/10.1162/tacl_a_00678
%P 875-891

Markdown (Informal)

[Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?](https://aclanthology.org/2024.tacl-1.49) (Aggazzotti et al., TACL 2024)

• Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts? (Aggazzotti et al., TACL 2024)

ACL

• Cristina Aggazzotti, Nicholas Andrews, and Elizabeth Allyn Smith. 2024. Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?. Transactions of the Association for Computational Linguistics, 12:875–891.