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A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls — arXiv:2608.28040

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28040
License (as archived): CC0-1.0
Archived: 2026-10-17T10:56:35.276Z
SHA-256 of the archived text: 3b37032394a63f424ae65f57018c5b8f0d2c722dcddf2866972296716b9dead9
Stable URL of this page: https://metraipolis.com/documento/dc_7faf5ecbec09

How to cite

A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls — arXiv:2608.28040 — arXiv (en), https://arxiv.org/abs/2608.28040 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7faf5ecbec09 (Metraipolis archive dc_7faf5ecbec09, sha256 3b37032394a63f424ae65f57018c5b8f0d2c722dcddf2866972296716b9dead9)

BibTeX

@misc{dc7faf5ecbec09,
  title = {A Shaky Voice Is Not Always a Dodge: Benchmarking Textual and Vocal Evasion Detection in Earnings Calls — arXiv:2608.28040},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28040},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7faf5ecbec09; sha256 3b37032394a63f424ae65f57018c5b8f0d2c722dcddf2866972296716b9dead9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28040. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Existing approaches to evasion detection in earnings calls focus on textual transcripts, treating evasion as a single-dimensional phenomenon. We argue that evasion in spoken communication is inherently multidimensional: beyond what executives say, how they say it carries independent and complementary information. To study these dimensions jointly, we introduce DualEvasion, a benchmark for evasion detection across text and audio in earnings call Q&A. The benchmark contains 505 annotated question-answer pairs from 60 earnings calls, each with two independent labels: textual evasion (direct vs. evasive) and vocal cues operationalized as speaker confidence (confident vs. unconfident). Our experiments show that state-of-the-art multimodal models struggle to detect vocal confidence, particularly on unconfident responses. Our analysis suggests these models interpret acoustic cues in isolation rather than relative to each speaker's baseline. Providing speaker-level references yields modest improvements, but a substantial gap with human performance remains.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Sound (cs.SD).
Pagina del lavoro: https://arxiv.org/abs/2608.28040 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:47:33.126Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).