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This paper investigates the utility of acoustic features (pitch, jitter, hesitation) for predicting stock market volatility from corporate earnings calls. They find that integrating acoustic features with an NLP baseline *decreases* performance in predicting tail-risk downside events, dropping recall from 66.25% to 47.08%. The authors attribute this to "Acoustic Camouflage," where trained speakers' vocal regulation introduces noise that disrupts multimodal meta-learners.
Trained speakers' vocal regulation can actively degrade the performance of multimodal financial risk prediction models, turning potentially useful acoustic features into misleading noise.
In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attempted to apply these acoustic frameworks to corporate earnings calls to predict catastrophic stock market volatility. In this study, we empirically investigate the limits of acoustic feature extraction (pitch, jitter, and hesitation) when applied to highly trained speakers in in-the-wild teleconference environments. Utilizing a two-stream late-fusion architecture, we contrast an acoustic-based stream with a baseline Natural Language Processing (NLP) stream. The isolated NLP model achieved a recall of 66.25% for tail-risk downside events. Surprisingly, integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%. We identify this degradation as Acoustic Camouflage, where media-trained vocal regulation introduces contradictory noise that disrupts multimodal meta-learners. We present these findings as a boundary condition for speech processing applications in high-stakes financial forecasting.