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New SPARC benchmark reveals LLMs can identify speakers from text alone

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SPARC, a new benchmark corpus designed to assess the amount of speaker and demographic information retained in transcribed speech. SPARC, derived from two podcast corpora, includes over 12,500 speakers and 1.5 million utterances, with labels for sex, age, and accent. Experiments show that large language models can distinguish between same-speaker pairs using only text, and can also verify sex and accent labels above chance levels. Releasing shorter text excerpts (50 words) appears to most effectively reduce the risk of speaker linkage. AI

IMPACT Highlights potential privacy risks in transcribed speech and the capabilities of LLMs in identifying personal attributes from text.

RANK_REASON The cluster contains an academic paper introducing a new benchmark dataset and evaluation of models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SPARC benchmark reveals LLMs can identify speakers from text alone

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The cluster contains an academic paper introducing a new benchmark dataset and evaluation of models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    SPARC: Spoken Profile Authorship Recognition Corpus

    Transcribed speech retains speaker and demographic information that large language models can exploit for verification, though releasing only brief text excerpts reduces linkage risk.

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