# Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2012.05429 · HTML: https://mlsanity.com/paper/2012.05429/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

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

- [Faster Adaptive Federated Learning](https://mlsanity.com/paper/2212.00974/) (2022)
- [CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition](https://mlsanity.com/paper/2307.15432/) (2023)
- [AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models](https://mlsanity.com/paper/2409.18339/) (2024)
- [Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels](https://mlsanity.com/paper/2101.03477/) (2021)
- [Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment](https://mlsanity.com/paper/2412.20821/) (2024)
- [Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition](https://mlsanity.com/paper/2302.08650/) (2023)
- [Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction](https://mlsanity.com/paper/2603.08230/) (2026)

## Abstract (arXiv)

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.