# Iterative Prototype Refinement for Ambiguous Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2408.00325 · HTML: https://mlsanity.com/paper/2408.00325/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2409.00815/) (2024)
- [Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding](https://mlsanity.com/paper/2412.16507/) (2024)

## Abstract (arXiv)

Recognizing emotions from speech is a daunting task due to the subtlety and ambiguity of expressions. Traditional speech emotion recognition (SER) systems, which typically rely on a singular, precise emotion label, struggle with this complexity. Therefore, modeling the inherent ambiguity of emotions is an urgent problem. In this paper, we propose an iterative prototype refinement framework (IPR) for ambiguous SER. IPR comprises two interlinked components: contrastive learning and class prototypes. The former provides an efficient way to obtain high-quality representations of ambiguous samples. The latter are dynamically updated based on ambiguous labels -- the similarity of the ambiguous data to all prototypes. These refined embeddings yield precise pseudo labels, thus reinforcing representation quality. Experimental evaluations conducted on the IEMOCAP dataset validate the superior performance of IPR over state-of-the-art methods, thus proving the effectiveness of our proposed method.