The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Prediction
The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Prediction
This paper investigates the application of acoustic speech features (pitch, jitter, hesitation) combined with textual sentiment analysis for predicting catastrophic stock market volatility from corporate earnings calls. Using the MAEC dataset, the authors implement a two-stream late-fusion architecture with L1-regularized logistic regression for each modality and a meta-learner. The text-only model, leveraging a novel 'Sentiment Delta' metric from FinBERT, achieves a recall of 66.25% for tail-risk downside events. Surprisingly, integrating acoustic features via late fusion degrades performance to 47.08% recall. The authors attribute this to 'Acoustic Camouflage': media-trained executives suppress vocal stress markers, creating contradictory noise that misleads the multimodal meta-learner. The study highlights a critical boundary condition for computational paralinguistics in finance, showing that pure NLP metrics outperform acoustic features in cost-sensitive risk prediction. Limitations include VoIP compression artifacts that may further obscure genuine paralinguistic signals.
Highlights
- 1Identifies 'Acoustic Camouflage' where media-trained vocal regulation introduces noise that degrades multimodal fusion for financial risk prediction.
- 2Shows that integrating acoustic features via late fusion reduces recall from 66.25% (text-only) to 47.08%.
- 3Demonstrates that pure NLP metrics (Sentiment Delta) outperform clinical acoustic features in cost-sensitive downside risk prediction.
- 4Provides empirical boundary conditions for applying computational paralinguistics in high-stakes financial forecasting.
Methods
- MTwo-stream late-fusion architecture with L1-regularized logistic regression for acoustic and text streams, and an L2-regularized meta-learner.
- MFeature extraction: acoustic variance features (pitch variance, jitter variance, Mean NHR, unvoiced fraction variance) and textual Sentiment Delta from FinBERT.
- MTarget variable engineering: 5-day cumulative return bottom 15% as catastrophic event with asymmetric class weighting.
- M5-fold stratified cross-validation with minority class recall as primary metric.
Results
- RIsolated text stream (Sentiment Delta) achieved 66.25% recall for tail-risk downside events.
- RIsolated acoustic stream achieved 50.83% recall.
- RLate-fusion meta-learner achieved only 47.08% recall, significantly underperforming the text-only baseline.
- RNon-linear tree-based models overfitted to acoustic features, degrading out-of-sample recall.
- RL1 regularization heavily suppressed acoustic features, confirming linguistic dominance.
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