AmbER$^2$: Dual Ambiguity-Aware Emotion Recognition Applied to Speech and Text
Abstract
Emotion recognition is inherently ambiguous, with uncertainty arising both from rater disagreement and from discrepancies across modalities such as speech and text. There is growing interest in modeling rater ambiguity using label distributions. However, modality ambiguity remains underexplored, and multimodal approaches often rely on simple feature fusion without explicitly addressing conflicts between modalities. In this work, we propose AmbER$^2$, a dual ambiguity-aware framework that simultaneously models rater-level and modality-level ambiguity through a teacher-student architecture with a distribution-wise training objective. Evaluations on IEMOCAP and MSP-Podcast show that AmbER$^2$ consistently improves distributional fidelity over conventional cross-entropy baselines and achieves performance competitive with, or superior to, recent state-of-the-art systems. For example, on IEMOCAP, AmbER$^2$ achieves relative improvements of 20.3% on Bhattacharyya coefficient (0.83 vs. 0.69), 13.6% on R$^2$ (0.67 vs. 0.59), 3.8% on accuracy (0.683 vs. 0.658), and 4.5% on F1 (0.675 vs. 0.646). Further analysis across ambiguity levels shows that explicitly modeling ambiguity is particularly beneficial for highly uncertain samples. These findings highlight the importance of jointly addressing rater and modality ambiguity when building robust emotion recognition systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of AmbER$^2$, a dual ambiguity-aware emotion recognition framework that effectively models both rater and modality ambiguity, significantly improving the fidelity of emotion predictions in multimodal contexts. This work represents a meaningful advancement in the field of emotion recognition, particularly in its approach to handling the inherent complexities and uncertainties associated with emotional expression across different modalities.
Methodology
The proposed AmbER$^2$ framework introduces a dual ambiguity-aware approach to emotion recognition, effectively addressing both rater and modality ambiguity through a teacher-student architecture. This methodology is innovative as it combines distribution-wise training objectives with adaptive guidance from modality-specific heads, which is a significant advancement over traditional feature fusion methods. The use of a weighted consistency loss that adjusts the influence of modality experts based on their reliability is particularly noteworthy, contributing to a more nuanced understanding of emotional cues across different modalities.
Results & evaluation
The experiments conducted on the IEMOCAP and MSP-Podcast datasets are comprehensive and well-structured, demonstrating the effectiveness of the proposed framework against conventional baselines and state-of-the-art systems. The reported improvements in distributional metrics (e.g., Bhattacharyya coefficient, R²) and classification metrics (e.g., accuracy, F1 score) provide strong evidence of the framework's performance. The analysis across different ambiguity levels adds depth to the evaluation, showcasing the framework's robustness in handling varying degrees of uncertainty.
Reproducibility
The paper provides sufficient implementation details, including the architecture of the models, training parameters, and the datasets used. However, the absence of a publicly available code repository or demo URL limits reproducibility. Future work should consider releasing the code to facilitate validation and further exploration by the research community.
Limitations
One limitation is the reliance on specific datasets (IEMOCAP and MSP-Podcast), which may not fully represent the diversity of real-world emotional expressions across cultures and languages. Additionally, while the framework shows promise in handling ambiguity, the complexity of the model may pose challenges in real-time applications. The paper could also benefit from a more detailed discussion on the computational efficiency and scalability of the proposed approach.
Broader impact
The findings of this research have significant implications for the development of more robust emotion recognition systems, which can enhance human-machine interactions in various applications, including virtual assistants, mental health monitoring, and customer service automation. By addressing ambiguity in emotion recognition, the framework paves the way for more human-aligned affective computing systems that can better understand and respond to human emotions. The main contribution of this paper is the introduction of AmbER$^2$, a dual ambiguity-aware emotion recognition framework that effectively models both rater and modality ambiguity, significantly improving the fidelity of emotion predictions in multimodal contexts. This work represents a meaningful advancement in the field of emotion recognition, particularly in its approach to handling the inherent complexities and uncertainties associated with emotional expression across different modalities.
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