二元互动抑郁检测中基于片段表示的说话人感知时间聚合策略：一项基准研究

摘要

基于语音的抑郁症检测将短音频片段的特征压缩为单一的说话人级别决策，这一被称为“时间聚合”的步骤鲜少被单独研究。现有基准测试通常固定使用单个自监督编码器和单个手工选择的层，因此报告的性能提升可能反映的是整个处理流程而非聚合方法本身。我们提出了DEPOOL，一个受控的基准测试平台，在英语和普通话抑郁症语料库上比较了六种聚合架构与六个冻结语音主干网络，每个配置会自动学习哪些主干网络层更关键，而非手工固定选择某一层。在这72种配置构成的网格中，三分之一的配置会退化到为每个说话人预测同一个类别——这种失败既与主干网络有关也与聚合方法有关，而单次随机种子运行中最稳定的架构，在跨种子的多次训练中却变得不可靠。对主干网络和随机种子的鲁棒性，而非单一流程下的平均准确率，应成为临床语音中时间聚合的首要基准评估标准。

English

Speech-based depression detection compresses features from short audio segments into one speaker-level decision, a step called temporal aggregation rarely studied on its own. Most benchmarks fix a single self-supervised encoder and a single hand-picked layer, so a reported gain may reflect the pipeline rather than the aggregation method itself. We introduce DEPOOL, a controlled benchmark that compares six aggregation architectures with six frozen speech backbones on an English and a Mandarin depression corpus, where each configuration learns which backbone layers matter rather than fixing one by hand. Across the resulting 72-configuration grid, a third of configurations collapse into predicting a single class for every speaker, a failure tied to the backbone as much as to the method, and the architecture that is most stable in a single-seed run becomes unreliable when training repeats across seeds. Robustness to backbone and seed, rather than average accuracy across a single pipeline, should be a first-class benchmarking criterion for temporal aggregation in clinical speech.