This work shows that depression changes the correlation between features extracted from speech. Furthermore, it shows that using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs. The experiments were performed over the Androids Corpus, a publicly available dataset involving 112 speakers, including 58 people diagnosed with depression by professional psychiatrists. The results show that the models used in the experiments improve in terms of training speed and performance when fed with feature correlation matrices rather than with feature vectors. The relative reduction of the error rate ranges between 23.1% and 26.6% depending on the model. The probable explanation is that feature correlation matrices appear to be more variable in the case of depressed speakers. Correspondingly, such a phenomenon can be thought of as a depression marker.

翻译：本文表明，抑郁会改变从语音中提取的特征之间的相关性。此外，本文还指出，利用这一发现能够提升基于支持向量机（SVM）和长短期记忆网络（LSTM）的抑郁检测器的训练速度与性能。实验在Android语料库上进行，这是一个公开的数据集，包含112名说话者，其中58人由专业精神科医生诊断为抑郁症。结果表明，当输入特征相关性矩阵而非特征向量时，实验中使用的模型在训练速度和性能上均得到提升，错误率相对降低幅度在23.1%至26.6%之间，具体取决于模型。可能的解释是，对于抑郁说话者，特征相关性矩阵的变化性更大；相应地，这种现象可被视为一种抑郁标志物。