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.

翻译：本文提出利用自监督学习预训练的基础模型，解决语音自动抑郁症检测中训练数据稀疏的问题。首先分析了预训练基础模型不同层提取的自监督表示特征，揭示其对抑郁症检测的适用指标。随后通过微调基础模型，将自动语音识别与情感识别的知识迁移至抑郁症检测任务。结果表明，在融合语音识别模型的隐含表示与文本信息时，使用真实语音识别转录与真实转录所得检测性能相近。通过整合多个基础模型的表示，基于真实语音识别的抑郁症检测方法在DAIC-WOZ数据集上取得了当前最优结果。