ArticleComputer speech & language2024
Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement.
Article in Computer speech & language, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2025Pooled it
- Diagnostic accuracy of deep learning using speech samples in depression: a systematic review and meta-analysis.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol.JMIR research protocols · 2026Article
- A gender-emotion interaction multi-task network for depression recognition via transformer-based multimodal fusion.Frontiers in psychiatry · 2026Article
- SBT-Net: a tri-cue guided multimodal fusion framework for depression recognition.BioData mining · 2025Article
- Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression.Frontiers in psychiatry · 2025Article
- Speechformer-CTC: Sequential Modeling of Depression Detection with Speech Temporal Classification.Speech communication · 2024Article
- A Privacy-Preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech.CEUR workshop proceedings · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Speech signals are valuable biomarkers for assessing an individual's mental health, including identifying Major Depressive Disorder (MDD) automatically. A frequently used approach in this regard is to employ features related to speaker identity, such as speaker-embeddings. However, over-reliance on speaker identity features in mental health screening systems can compromise patient privacy. Moreover, some aspects of speaker identity may not be relevant for depression detection and could serve as a bias factor that hampers system performance. To overcome these limitations, we propose disentangling speaker-identity information from depression-related information. Specifically, we present four distinct disentanglement methods to achieve this - adversarial speaker identification (SID)-loss maximization (ADV), SID-loss equalization with variance (LEV), SID-loss equalization using Cross-Entropy (LECE) and SID-loss equalization using KL divergence (LEKLD). Our experiments, which incorporated diverse input features and model architectures, have yielded improved F1 scores for MDD detection and voice-privacy attributes, as quantified by Gain in Voice Distinctiveness
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