It Hears, It Sees too: Multi-Modal LLM for Depression Detection By Integrating Visual Understanding into Audio Language Models
Abstract
Depression is one of the most prevalent mental health disorders globally. In recent years, multi-modal data, such as speech, video, and transcripts, has been increasingly used to develop AI-assisted depression assessment systems. Large language models have further advanced this field due to their strong language understanding and generalization capabilities. However, conventional LLMs remain text-centric and cannot process the rich non-verbal cues found in audio and visual modalities, which are critical components in mental health evaluation. While multi-modal LLMs offer a promising direction, few are tailored for psychological applications. In this study, we propose a novel multi-modal LLM framework for depression detection. Our approach augments an audio language model with visual understanding and aligns audio-visual features at the timestamp level. This fine-grained alignment improves modeling of temporal dynamics across modalities while reducing the need for extensive training data and computational resources. Experiments on the DAIC-WoZ dataset demonstrate that our model outperforms both single-modality approaches and previous multi-modal methods. Moreover, the proposed framework can be extended to incorporate additional physiological signals, paving the way for broader clinical applications beyond mental health.
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 multi-modal large language model for depression detection that integrates audio, visual, and textual modalities, demonstrating superior performance compared to existing methods. The innovative approach and thorough experimental validation contribute meaningfully to the field of machine learning and mental health applications.
Methodology
The proposed methodology introduces a multi-modal large language model (MLLM) for depression detection that effectively integrates audio and visual modalities with textual data. The use of timestamp-level alignment for audio-visual features is innovative, enhancing the model's ability to capture temporal dynamics relevant to depression detection. The method of augmenting an audio language model with visual understanding through parameter-efficient fine-tuning (PEFT) is a significant advancement, particularly in the context of mental health applications where data scarcity is a concern. The three-stage training process is well-structured and addresses potential overfitting by leveraging self-supervised learning and contrastive alignment tasks.
Results & evaluation
The experiments conducted on the DAIC-WoZ dataset are robust, demonstrating a clear comparison with both single-modality and existing multi-modal approaches. The results indicate that the proposed model outperforms previous methods, achieving a notable F1 score of 0.789. The comprehensive evaluation includes ablation studies that effectively isolate the contributions of each modality, providing insights into the model's performance dynamics. The use of a well-established dataset adds credibility to the findings.
Reproducibility
The paper provides detailed implementation details, including the training pipeline, hyperparameters, and the use of specific models (Qwen2-Audio). However, the absence of a publicly available code repository or demo limits the reproducibility of the results. The methodology is described in sufficient detail for replication, but access to the trained model or code would enhance reproducibility.
Limitations
One limitation is the reliance on the DAIC-WoZ dataset, which may not fully represent the diversity of depression presentations across different populations. The model's performance may vary with different datasets or real-world applications. Additionally, the approach may not generalize well to other mental health disorders without further adaptation. The removal of interviewer utterances during data augmentation could lead to loss of contextual information, potentially impacting model performance.
Broader impact
The implications of this work are significant for mental health assessment, offering a novel approach to depression detection that leverages multi-modal data. The framework has the potential to improve early detection and intervention strategies in clinical settings. However, ethical considerations regarding the use of AI in mental health must be addressed, particularly concerning privacy and the potential for misdiagnosis. This paper presents a novel multi-modal large language model for depression detection that integrates audio, visual, and textual modalities, demonstrating superior performance compared to existing methods. The innovative approach and thorough experimental validation contribute meaningfully to the field of machine learning and mental health applications.
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