Academic Journal
Investigation of Speech Landmark Patterns for Depression Detection
| Title: | Investigation of Speech Landmark Patterns for Depression Detection |
|---|---|
| Authors: | Huang, Z, Epps, J, Joachim, D |
| Source: | IEEE Transactions on Affective Computing. 13:666-679 |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| Publication Year: | 2022 |
| Subject Terms: | anzsrc-for: 1702 Cognitive Sciences, 0301 basic medicine, 4608 Human-Centred Computing, Depression, anzsrc-for: 46 Information and Computing Sciences, anzsrc-for: 4603 Computer vision and multimedia computation, 02 engineering and technology, Mental Illness, Brain Disorders, anzsrc-for: 0806 Information Systems, anzsrc-for: 4608 Human-Centred Computing, 03 medical and health sciences, Mental Health, 46 Information and Computing Sciences, anzsrc-for: 4602 Artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, anzsrc-for: 0801 Artificial Intelligence and Image Processing |
| Description: | The massive and growing burden imposed on modern society by depression has motivated investigations into early detection through automated, scalable and non-invasive methods, including those based on speech. However, speech-based methods that capture articulatory information effectively across different recording devices and in naturalistic environments are still needed. This article proposes two feature sets associated with speech articulation events based on counts and durations of sequential landmark groups or n-grams. Statistical analysis of the duration-based features reveals that durations from several consecutive landmark bigrams and onset-offset landmark pairs are significant in discriminating depressed from non-depressed speakers. In addition to investigating different normalization approaches and values of n for landmark n-gram features, experiments across different elicitation tasks suggest that the features can be tailored to capture different articulatory aspects of depressed voices. Evaluations of both landmark duration features and landmark n-gram features on the DAIC-WOZ and SH2 datasets show that they are highly effective, either alone or fused, relative to existing approaches. |
| Document Type: | Article |
| File Description: | application/pdf |
| ISSN: | 2371-9850 |
| DOI: | 10.1109/taffc.2019.2944380 |
| Access URL: | https://ieeexplore.ieee.org/document/8861018 |
| Rights: | IEEE Copyright CC BY NC ND |
| Accession Number: | edsair.doi.dedup.....8aa659c1a35640a85a8045f3aaf16699 |
| Database: | OpenAIRE |
| ISSN: | 23719850 |
|---|---|
| DOI: | 10.1109/taffc.2019.2944380 |