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Evidence record 1364 · automatically gathered

Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition

Instruction-following audio language models (ALMs) can be augmented with explicit acoustic cues, yet it remains unclear whether such cues are used in a grounded way when the raw audio is already available. We study this question in speech emotion recognition (SER) by deriving six interpretable acoustic concept tokens from the standardised eGeMAPS paralinguistic feature set. These tokens summarise energy, pitch, dynamics, brightness, formants, and voice quality, and are appended to the textual pr

Record details

Published: 5 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (5 June 2026), “Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition,” evidence record 1364, https://ethics.ai/record/1364 (originally published by arXiv).

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