ERM-MinMaxGAP: Benchmarking and Mitigating Gender Bias in Multilingual Multimodal Speech-LLM Emotion Recognition
Abstract
Speech emotion recognition (SER) systems can exhibit gender-related performance disparities, but how such bias manifests in multilingual speech LLMs across languages and modalities is unclear. We introduce a novel multilingual, multimodal benchmark built on MELD-ST, spanning English, Japanese, and German, to quantify language-specific SER performance and gender gaps. We find bias is strongly language-dependent, and multimodal fusion does not reliably improve fairness. To address these, we propose ERM-MinMaxGAP, a fairness-informed training objective, which augments empirical risk minimization (ERM) with a proposed adaptive fairness weight mechanism and a novel MinMaxGAP regularizer on the maximum male-female loss gap within each language and modality. Building upon the Qwen2-Audio backbone, our ERM-MinMaxGAP approach improves multilingual SER performance by 5.5% and 5.0% while reducing the overall gender bias gap by 0.1% and 1.4% in the unimodal and multimodal settings, respectively.
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
This paper presents a novel benchmark and a fairness-aware training objective for mitigating gender bias in multilingual multimodal speech emotion recognition systems. The technical contributions and methodology are robust, addressing a pressing issue in the field of machine learning and AI.
Methodology
The proposed methodology, ERM-MinMaxGAP, is a significant advancement in addressing gender bias in multilingual multimodal speech emotion recognition (SER). The integration of empirical risk minimization with a fairness regularization term that focuses on the maximum male-female loss gap is innovative. The adaptive fairness weight mechanism further enhances the robustness of the training process, allowing for dynamic adjustments based on the model's performance. The detailed description of the MinMaxGAP regularizer and its implementation demonstrates a thorough understanding of the complexities involved in SER tasks, particularly in a multilingual context.
Results & evaluation
The experimental setup is well-structured, utilizing the MELD-ST dataset to benchmark the proposed method against existing models. The results indicate that ERM-MinMaxGAP not only improves SER performance but also reduces gender disparity effectively across different languages and modalities. The ablation studies provide valuable insights into the contributions of each component of the proposed method, reinforcing the effectiveness of the MinMaxGAP regularization approach.
Reproducibility
The paper states that all code, data, and models will be released upon acceptance, which is a positive aspect for reproducibility. However, specific implementation details regarding the training process, hyperparameters, and dataset preparation are provided, which aids in replicating the experiments. The clarity in methodology and results presentation supports reproducibility.
Limitations
One limitation is that while the proposed method shows improvements in SER and fairness, it does not achieve the minimum post-hoc gender gap in every setting, indicating that the approach may not be universally applicable across all datasets or languages. Additionally, the reliance on a specific dataset (MELD-ST) may limit the generalizability of the findings.
Broader impact
The implications of this research are significant, as it addresses a critical issue of fairness in AI systems, particularly in emotion recognition, which has applications in various fields such as mental health assessment, customer service, and human-computer interaction. By improving fairness in SER systems, this work contributes to the development of more equitable AI technologies that can better serve diverse populations. This paper presents a novel benchmark and a fairness-aware training objective for mitigating gender bias in multilingual multimodal speech emotion recognition systems. The technical contributions and methodology are robust, addressing a pressing issue in the field of machine learning and AI.
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