Self-supervised representations in speech-based depression detection

2023-05-20 · Wen Wu, Chao Zhang, Philip C. Woodland

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.

📄 PDF Abstract BibTeX arXiv:2305.12263

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Tasks

Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Depression DetectionEmotion Recognition Self-Supervised Learning speech-recognition Speech Recognition Transfer Learning

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