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Depression level prediction via textual and acoustic analysis

Computers in Biology and Medicine 2025年 190卷 110009-110009页

作者： Hong, Jisun Lee, Jihun Choi, Daegil Jung, Jaehyo AI Healthcare Research Center Department of IT Fusion Technology Chosun University 309 Pilmun-daero Dong-gu Gwangju61452 Korea Republic of Department of Bigdata and Artificial Intelligence Eulji Universtiy Seongnam-si13135 Korea Republic of

Extensive research on automatic depression diagnosis has utilized video data to capture related cues, but data collection is challenging because of privacy concerns. By contrast, voice data offer a less-intrusive assessment method and can be analyzed for features such as simple tones, the expression of negative emotions, and a focus on oneself. Recent advancements in multimodal depression-level prediction using speech and text data have gained traction, but most studies overlook the temporal alignment of these modalities, limiting their analysis of the interaction between speech content and intonation. To overcome these limitations, this study introduces timestamp-integrated multimodal encoding for depression (TIMEX-D) which synchronizes the acoustic features of human speech with corresponding text data to predict depression levels on the basis of their relationship. TIMEX-D comprises three main components: a timestamp extraction block that extracts timestamps from speech and text, a multimodal encoding block that extends positional encoding from transformers to mimic human speech recognition, and a depression analysis block that predicts depression levels while reducing model complexity compared with existing transformers. In experiments using the DAIC-WOZ and EDAIC datasets, TIMEX-D achieved accuracies of 99.17 % and 99.81 %, respectively, outperforming previous methods by approximately 13 %. The effectiveness of TIMEX-D in predicting depression levels can enhance mental health diagnostics and monitoring across various contexts. © 2025 Elsevier Ltd

关键词： Depression analysis block Human speech recognition system multimodal encoding block Positional encoding Timestamp extraction block TIMEX-D Transformer encoder

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