arXiv Computation and Language By Mirae Kim, Seonghun Jeong, Youngjun Kwak

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

Read the original on arXiv Computation and Language →

The paper introduces DualEvasion, a benchmark that evaluates evasion detection in earnings call Q&A using both textual transcripts and vocal cues. It contains 505 annotated question‑answer pairs from 60 calls, each labeled for textual evasion (direct vs. evasive) and speaker confidence (confident vs. unconfident). Experiments show that current multimodal models struggle to detect vocal confidence, especially in unconfident responses, and that providing speaker‑level references only modestly improves performance, leaving a significant gap compared to humans.

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