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    <titleInfo>
        <title>The Impact of Automatic Speech Transcription on Speaker Attribution</title>
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    <name type="personal">
        <namePart type="given">Cristina</namePart>
        <namePart type="family">Aggazzotti</namePart>
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        <namePart type="given">Matthew</namePart>
        <namePart type="family">Wiesner</namePart>
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    <name type="personal">
        <namePart type="given">Elizabeth</namePart>
        <namePart type="given">Allyn</namePart>
        <namePart type="family">Smith</namePart>
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    <name type="personal">
        <namePart type="given">Nicholas</namePart>
        <namePart type="family">Andrews</namePart>
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    <abstract>Speaker attribution from speech transcripts is the task of identifying a speaker from the transcript of their speech based on patterns in their language use. This task is especially useful when the audio is unavailable (e.g., deleted) or unreliable (e.g., anonymized speech). Prior work in this area has primarily focused on the feasibility of attributing speakers using transcripts produced by human annotators. However, in real-world settings, one often only has more errorful transcripts produced by automatic speech recognition (ASR) systems. In this paper, we conduct what is, to our knowledge, the first comprehensive study of the impact of automatic transcription on speaker attribution performance. In particular, we study the extent to which speaker attribution performance degrades in the face of transcription errors, as well as how properties of the ASR system impact attribution. We find that attribution is surprisingly resilient to word-level transcription errors and that the objective of recovering the true transcript is minimally correlated with attribution performance. Overall, our findings suggest that speaker attribution on more errorful transcripts produced by ASR is as good, if not better, than attribution based on human-transcribed data, possibly because ASR transcription errors can capture speaker-specific features revealing of speaker identity.</abstract>
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    <identifier type="doi">10.1162/tacl.a.54</identifier>
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        <date>2025</date>
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%0 Journal Article
%T The Impact of Automatic Speech Transcription on Speaker Attribution
%A Aggazzotti, Cristina
%A Wiesner, Matthew
%A Smith, Elizabeth Allyn
%A Andrews, Nicholas
%J Transactions of the Association for Computational Linguistics
%D 2025
%V 13
%I MIT Press
%C Cambridge, MA
%F aggazzotti-etal-2025-impact
%X Speaker attribution from speech transcripts is the task of identifying a speaker from the transcript of their speech based on patterns in their language use. This task is especially useful when the audio is unavailable (e.g., deleted) or unreliable (e.g., anonymized speech). Prior work in this area has primarily focused on the feasibility of attributing speakers using transcripts produced by human annotators. However, in real-world settings, one often only has more errorful transcripts produced by automatic speech recognition (ASR) systems. In this paper, we conduct what is, to our knowledge, the first comprehensive study of the impact of automatic transcription on speaker attribution performance. In particular, we study the extent to which speaker attribution performance degrades in the face of transcription errors, as well as how properties of the ASR system impact attribution. We find that attribution is surprisingly resilient to word-level transcription errors and that the objective of recovering the true transcript is minimally correlated with attribution performance. Overall, our findings suggest that speaker attribution on more errorful transcripts produced by ASR is as good, if not better, than attribution based on human-transcribed data, possibly because ASR transcription errors can capture speaker-specific features revealing of speaker identity.
%R 10.1162/tacl.a.54
%U https://aclanthology.org/2025.tacl-1.72/
%U https://doi.org/10.1162/tacl.a.54
%P 1578-1596

Markdown (Informal)

[The Impact of Automatic Speech Transcription on Speaker Attribution](https://aclanthology.org/2025.tacl-1.72/) (Aggazzotti et al., TACL 2025)

• The Impact of Automatic Speech Transcription on Speaker Attribution (Aggazzotti et al., TACL 2025)

ACL

• Cristina Aggazzotti, Matthew Wiesner, Elizabeth Allyn Smith, and Nicholas Andrews. 2025. The Impact of Automatic Speech Transcription on Speaker Attribution. Transactions of the Association for Computational Linguistics, 13:1578–1596.