The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Prediction
For financial risk prediction, this work identifies a boundary condition where acoustic features harm performance, highlighting limitations of current speech processing in high-stakes settings.
The study investigates acoustic features for predicting stock market volatility from earnings calls, finding that integrating acoustic features degrades performance (recall drops from 66.25% to 47.08%) due to trained vocal regulation, termed Acoustic Camouflage.
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.