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Conference Paper
A Comparative Study of AI Models for Depression Assessment using Voice Features
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- Authors
- Eunkyoung Jeon, Sehwan Moon, Seihyoung Lee, Aram Lee, Sanghyun Kim, Kiwon Park, Jeong Eun Kim
- Issue Date
- 2023-12
- Citation
- International Conference on Bioinformatics and Biomedicine (BIBM) 2023, pp.4898-4899
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, there has been a significant amount of research conducted on classifying depression by extracting features from voice data. In this study, we enhanced the dataset by segmenting the voice data into 4-second intervals, followed by the extraction of voice features using MFCC (Mel Frequency Cepstral Coefficients) and Mel-spectrogram. After extracting these voice features, we compared the performance depending on the models and found that using MFCC features and classifying depression with the XGBoost model yielded the best performance. Looking forward, we aim to enhance the model’s performance by utilizing multimodal data, including voice, video, and text data.
- KSP Keywords
- Best performance, MFCC features, Mel-frequency Cepstral Coefficient(MFCC), Voice Data, Voice features, comparative study, multimodal data, text data