Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.contributor.advisor | Mak, Man Wai (EEE) | en_US |
| dc.creator | Zhang, Tao | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14052 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Spoken language processing and natural language processing for depression detection | en_US |
| dcterms.abstract | Depression detection remains challenging due to the lack of standardized diagnostic methods, with clinical assessments by healthcare professionals being the primary approach. This method is inherently subjective and may not be reproducible. Current research in intelligent depression detection often focuses on isolated factors, which can result in a limited understanding of the condition. Our study enhanced a previous fusion model by incorporating pre-trained models to extract features from audio and text data. Specifically, we utilize the EATD-corpus and employ two pre-trained models: Wav2vec2.0 for audio feature extraction and RoBERTa for text feature extraction. Both models are fine-tuned using Chinese datasets to optimize their performance on the EATD Corpus. Additionally, we employ a deep neural network (DNN) model to combine and classify text and audio data into depressed and non-depressed data simultaneously. The final results of our model demonstrate a significant improvement in the extraction of relevant features from both audio and text data for depression detection. | en_US |
| dcterms.extent | v, 38 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8719.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.91 MB | Adobe PDF | View/Open |
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