Lie to me: Detecting Managerial Evasiveness in Earnings Calls via Conversational Audio Encoders
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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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Prosodic cues can convey task-relevant information that alters the trajectory and outcome of a task-oriented dialogue, even when the words themselves remain unchanged. Yet existing benchmarks typically evaluate prosodic perception, response appropriateness, and task-oriented dialogue in isolation, making it difficult to test whether prosodic evidence changes downstream decisions.