# OmniVox: Zero-Shot Emotion Recognition with Omni-LLMs

arXiv: https://arxiv.org/abs/2503.21480 · HTML: https://mlsanity.com/paper/2503.21480/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://mlsanity.com/paper/2503.01743/) (2025)
- [Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning](https://mlsanity.com/paper/2406.11161/) (2024)
- [Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion](https://mlsanity.com/paper/2404.17858/) (2024)
- [Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum](https://mlsanity.com/paper/2404.17862/) (2024)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://mlsanity.com/paper/2306.01505/) (2023)
- [TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2401.12987/) (2024)
- [InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2309.11911/) (2023)
- [Temporal-Frequency State Space Duality: An Efficient Paradigm for Speech Emotion Recognition](https://mlsanity.com/paper/2412.16904/) (2024)
- [LLM supervised Pre-training for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2501.11468/) (2025)
- [Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues](https://mlsanity.com/paper/2502.10973/) (2025)
- [Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment](https://mlsanity.com/paper/2412.20821/) (2024)
- [Synthetic Audio Helps for Cognitive State Tasks](https://mlsanity.com/paper/2502.06922/) (2025)

## Built on by (most cited first, 7 of 7)

- [Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting](https://mlsanity.com/paper/2508.02429/) (2025)
- [Scaling Ambiguity: Augmenting Human Annotation in Speech Emotion Recognition with Audio-Language Models](https://mlsanity.com/paper/2601.14620/) (2026)
- [VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs](https://mlsanity.com/paper/2603.08936/) (2026)
- [Investigation for Relative Voice Impression Estimation](https://mlsanity.com/paper/2602.14172/) (2026)
- [Plug-and-Play Emotion Graphs for Compositional Prompting in Zero-Shot Speech Emotion Recognition](https://mlsanity.com/paper/2509.25458/) (2025)
- [VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2605.18547/) (2026)
- [When to Call an LLM: A Confidence-Gated Hybrid for Cost-Effective Emotion Recognition in Conversational AI](https://mlsanity.com/paper/2609.17977/) (2026)

## Abstract (arXiv)

The use of omni-LLMs (large language models that accept any modality as input), particularly for multimodal cognitive state tasks involving speech, is understudied. We present OmniVox, the first systematic evaluation of four omni-LLMs on the zero-shot emotion recognition task. We evaluate on two widely used multimodal emotion benchmarks: IEMOCAP and MELD, and find zero-shot omni-LLMs outperform or are competitive with fine-tuned audio models. Alongside our audio-only evaluation, we also evaluate omni-LLMs on text only and text and audio. We present acoustic prompting, an audio-specific prompting strategy for omni-LLMs which focuses on acoustic feature analysis, conversation context analysis, and step-by-step reasoning. We compare our acoustic prompting to minimal prompting and full chain-of-thought prompting techniques. We perform a context window analysis on IEMOCAP and MELD, and find that using context helps, especially on IEMOCAP. We conclude with an error analysis on the generated acoustic reasoning outputs from the omni-LLMs.