Paper: arXiv 2604.14619

Authors: Dhruvin Dungrani, Disha Dungrani

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on standardized statistical techniques (logistic regression, L1/L2 regularization) rather than complex derivations, but provides solid empirical value through its ablation study and demonstration of performance degradation in a real-world dataset scenario. The analysis successfully identifies a practical ‘boundary condition’ for multimodal models that challenges common assumptions in financial NLP.

Research Flowchart

  flowchart TD
    A[<b>Goal:</b> Evaluate acoustic features for predicting financial tail-risk] --> B[<b>Data:</b> Corporate earnings call recordings]
    B --> C[<b>Methodology:</b> Two-stream late-fusion architecture]
    C --> D[<b>Processes:</b> Acoustic Stream (Pitch, Jitter, Hesitation) vs. NLP Stream]
    D --> E[<b>Finding:</b> NLP Baseline (66.25% recall)]
    E --> F[<b>Outcome:</b> 'Acoustic Camouflage' - Fusion degrades performance to 47.08%]

Cited References (7)

  1. What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues (2019)
  2. MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction (2020)
  3. Vocal communication of emotion: A review of research paradigms (2003)
  4. Analyzing speech to detect financial misreporting (2012)
  5. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks (2011)
  6. FinBERT: Financial sentiment analysis with pre-trained language models (2019) — link
  7. VolTAGE: Volatility forecasting via text audio fusion with graph convolution networks for earnings calls (2020)

0 of 7 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.