# EmoQ: Speech Emotion Recognition via Speech-Aware Q-Former and Large Language Model

arXiv: https://arxiv.org/abs/2509.15775 · HTML: https://mlsanity.com/paper/2509.15775/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [HFS: Holistic Query-Aware Frame Selection for Efficient Video Understanding](https://mlsanity.com/paper/2512.11534/) (2025)
- [HyPASE: Hyperbolic Geometry for Parameter-Efficient Speech Emotion Fine-Tuning Framework for Large Audio-Language Models](https://mlsanity.com/paper/2608.04351/) (2026)
- [Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition](https://mlsanity.com/paper/2609.20081/) (2026)

## Abstract (arXiv)

The performance of speech emotion recognition (SER) is limited by the insufficient emotion information in unimodal systems and the feature alignment difficulties in multimodal systems. Recently, multimodal large language models (MLLMs) have made progress in SER. However, MLLMs still suffer from hallucination and misclassification problems in complex emotion reasoning. To address these problems, we propose an MLLM-based framework called EmoQ, which generates query embeddings that fuse multimodal information through an EmoQ-Former and uses multi-objective affective learning (MAL) to achieve co-optimization. The framework also provides a soft-prompt injection strategy to inject multimodal representations into the LLM. This end-to-end architecture achieves state-of-the-art performance on the IEMOCAP and MELD datasets, providing a new multimodal fusion paradigm for SER.