Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition

Saurabh Kataria; Xiao Hu

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arXiv · 2603.23057

Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition

Saurabh Kataria·Xiao Hu

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Abstract

Audio-Language Models (ALMs) are making strides in understanding speech and non-speech audio. However, domain-specialist Foundation Models (FMs) remain the best for closed-ended speech processing tasks such as Speech Emotion Recognition (SER). Using ALMs for Zero-shot SER is a popular choice, but their potential to work with specialists to achieve state-of-the-art (SOTA) performance remains unexplored. We propose ZS-Fuse, a late-fusion method that combines zero-shot emotion estimates from a dual-encoder ALM with specialist FMs. To handle ambiguity in emotions and sensitivity to prompt choice, 1) we use a simple prompt ensemble and 2) suggest a novel technique called prompt amplification, which repeats audio and text queries to discover stronger zero-shot capabilities. We demonstrate the efficacy of our technique by evaluating ZS-Fuse with three dual-encoder ALMs and two FMs, and report improvements over SOTA baselines, such as WavLM-Large, on three speech emotion recognition datasets.

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Saurabh Kataria, Xiao Hu. 2026-03-24. Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition. https://arxiv.org/abs/2603.23057

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