Multimodal Depression Detection Using AI-Based Behavioural Analysis:Implementation

Abstract

Depression is a common mental health disorderthatfrequentlygoesundetectedduetothesubjectivenature of traditional diagnostic methods and the limited availability of professional mental healthcare. A major challenge in identifying depression is that its symptoms are expressed through multiple behavioral signals, including language usage, speech patterns, andfacialexpressions,whicharedifficulttoanalyzeusingsingle-modality approaches. This paper introduces a multimodal deep learning framework thatintegratestextual,audio,andvisualinformationforeffective depression detection. The proposed system consists of three specialized components: a transformer-based model (DeBERTa) for analyzing textual data, a Wav2Vec-based network for ex-tracting acoustic features such as MFCCs, pitch, and energyfrom speech, and convolutional neural networks (ResNet and MobileNet) combined with temporal modeling for capturing visual cues from facial expressions. Toenhancepredictionperformance,alatefusionstrategywith anattentionmechanismisemployedtocombinetheoutputsfrom different modalities. The architecture is designed to efficiently handle multimodal inputs in a scalable manner. Experimental evaluation on benchmark datasets such as DAIC-WOZ shows that the proposed approach achieves improved accuracy and generalization compared to unimodal methods. Overall, the system provides a non-invasive and scalable approach for early depression detection, offering a practical alternative to traditional assessment techniques and supporting improved access to mental healthcare.

Introduction

This paper proposes a multimodal depression detection framework that combines textual, audio, and visual data to improve the accuracy and reliability of identifying depression. Depression is a common mental health disorder that affects emotional well-being, cognition, and daily functioning, yet many cases remain undetected due to reliance on subjective assessments and limited access to mental health services. Traditional computational approaches often analyze only one type of data, such as speech, text, or facial expressions, which limits their ability to capture the complex nature of depression.

To address these limitations, the proposed system integrates multiple behavioral signals within a unified deep learning framework. The system processes interview-based recordings and extracts meaningful features from each modality. A DeBERTa transformer model is used to analyze textual data and capture contextual and semantic information. For audio analysis, Wav2Vec extracts speech-related characteristics such as pitch, energy, and MFCC features. Visual information is processed using CNN-based architectures with temporal modeling to identify facial expressions and behavioral patterns associated with depression.

The architecture consists of separate processing modules for text, audio, and video, followed by a fusion layer and a classification layer. The fusion module employs a late-fusion strategy combined with an attention mechanism, allowing the system to dynamically assign importance to each modality based on its contribution to the prediction. The integrated features are then passed to a classifier that predicts the level of depression.

The methodology includes data acquisition from interview recordings, preprocessing of text, speech, and video data, feature extraction using modality-specific deep learning models, and supervised training on labeled depression datasets. Transformer models learn contextual representations from text, Wav2Vec extracts acoustic features from speech, and CNNs capture facial and behavioral cues from video frames. The outputs are fused using attention-based weighting to create a comprehensive representation for classification.

The framework is implemented using Python, TensorFlow, PyTorch, and OpenCV. Text is cleaned and tokenized, audio signals are normalized and converted into MFCC features, and videos are processed frame by frame. Hybrid CNN-LSTM models are used to capture both spatial and temporal patterns, while the Adam optimizer and regularization techniques such as dropout help improve model performance and generalization.

Experimental results demonstrate strong performance, achieving 92.7% accuracy, with a Character Error Rate (CER) of 1.2% and a Word Error Rate (WER) of 7.3%. These results indicate that combining multiple modalities significantly enhances depression detection compared to single-modality approaches.

Conclusion

The effectiveness of the proposed multimodal frameworkis assessed using standard evaluation measures such as Mean AbsoluteError(MAE),RootMeanSquareError(RMSE),and overall accuracy. Theobtainedresultshighlightthattheuseofmultiple data sources provides a clear advantage over single-modality approaches. By jointly analyzing textual content, speech char-acteristics, and visual cues, the system is able to represent diverse behavioral patterns that are typically associated with depressive conditions. As illustrated in Fig. 2, the confusion matrix reflects strong classificationcapability,withonlyasmallnumberofincorrect predictions.Thisindicatesthatthemodelisabletodistinguish between classes with a high level of consistency. In addition, the attention-based fusion strategy contributes to performance improvement by adaptively emphasizing the most informative modality for each prediction. However,thesystemisnotwithoutlimitations.Theintegra-tion of multiple modalities increases computational overhead andrequireswell-aligned,high-qualitydataforoptimalperfor-mance. Future work can focus on reducing model complexity and improving efficiency, as well as enhancing generalization across different datasets and real-world scenarios.

References

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Copyright

Copyright © 2026 Mahalakshmi V S, Anagha A B, T P Devaprayag, Maneeshkumar G, Binu Balakrishnan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.