Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

A study on Acoustic Cue Alignment in Audio Language Models (ALMs) for Speech Emotion Recognition (SER) investigates how these models utilize explicit acoustic cues alongside raw audio. Researchers derived six interpretable acoustic concept tokens—summarizing energy, pitch, dynamics, brightness, formants, and voice quality—from the *eGeMAPS* feature set. These tokens were appended to textual prompts for ALMs like Qwen2-Audio, Qwen2.5-Omni, and Audio Flamingo 3 (AF3), while the audio input remained constant. Across FAU-Aibo and IEMOCAP benchmarks, aligned tokens consistently improved unweighted average recall (UAR), with AF3 achieving UAR$^{+}=.776$ on IEMOCAP. Conversely, shuffled, conflicting, or corrupted tokens reduced performance and shifted errors towards neutral. Crucially, predictions did not collapse under strong token perturbations, indicating ALMs are sensitive to symbolic cues but maintain grounding in the audio signal.

Key takeaway

For Machine Learning Engineers developing Audio Language Models for Speech Emotion Recognition, you should consider augmenting your ALM prompts with structured acoustic concept tokens. This approach, using *eGeMAPS*-derived features, consistently improves UAR and offers a practical method to probe model robustness and interpretability. By testing token perturbations, you can ensure your models effectively integrate symbolic cues while remaining anchored to the audio signal, preventing over-reliance on potentially misleading textual inputs.

Key insights

Audio Language Models integrate explicit acoustic concept tokens with raw audio, improving Speech Emotion Recognition without over-relying on symbolic cues.

Principles

• Aligned acoustic concept tokens consistently enhance ALM performance in SER.
• ALMs demonstrate robust fallback, maintaining audio grounding even with contradictory symbolic cues.
• Token-only interventions effectively probe ALM cue integration, robustness, and interpretability.

Method

Derive six *eGeMAPS*-based acoustic concept tokens by binning features. Append these categorical tokens to ALM prompts. Evaluate ALM predictions under aligned, shuffled, conflicting, and corrupted token conditions while keeping audio fixed.

In practice

• Implement *eGeMAPS*-derived concept tokens to boost ALM performance in affective computing tasks.
• Apply token perturbation tests to diagnose ALM sensitivity and robustness to auxiliary information.

Topics

• Audio Language Models
• Speech Emotion Recognition
• eGeMAPS Features
• Acoustic Cue Alignment
• Token Interventions
• Computational Paralinguistics

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.