# Self-supervised representations in speech-based depression detection Source: [Self-supervised representations in speech-based depression detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:31:17.072Z Authors: Wen Wu, Chao Zhang, Philip C. Woodland Published: 2023-05-20T18:41:13.000Z ## Abstract This paper proposes handling training data sparsity in speech-based automatic depression detection (SDD) using foundation models pre-trained with self-supervised learning (SSL). An analysis of SSL representations derived from different layers of pre-trained foundation models is first presented for SDD, which provides insight to suitable indicator for depression detection. Knowledge transfer is then performed from automatic speech recognition (ASR) and emotion recognition to SDD by fine-tuning the foundation models. Results show that the uses of oracle and ASR transcriptions yield similar SDD performance when the hidden representations of the ASR model is incorporated along with the ASR textual information. By integrating representations from multiple foundation models, state-of-the-art SDD results based on real ASR were achieved on the DAIC-WOZ dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Depression Detection on Depression Detection](): Depression Detection; Depression Detection; Mean ECE: 0.183. - [Depression Detection on DAIC-WOZ (dev)](): Depression Detection; DAIC-WOZ (dev); F1 (avg): 70. ## Original sources and implementation [Original paper]() [PDF]()