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：本研究揭示了抑郁症会改变语音特征间的相关性。进一步表明，利用这一发现可提升基于支持向量机（SVM）和长短期记忆网络（LSTM）的抑郁检测模型的训练速度与性能。实验在公开数据集Androids Corpus上进行，该数据集包含112名说话者，其中58人经专业精神科医生确诊为抑郁症。结果表明，当使用特征相关矩阵而非特征向量作为输入时，实验模型的训练速度与性能均有所提升。根据模型不同，错误率相对降低幅度介于23.1%至26.6%之间。可能的解释是：抑郁说话者的特征相关矩阵呈现出更高的变异性。因此，这一现象可视为抑郁症的标记。