Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an objective marker for the early recognition of depression. For this reason, many studies have been developed aiming to recognize depression through speech. However, existing methods rely on the usage of only the spontaneous speech neglecting information obtained via read speech, use transcripts which are often difficult to obtain (manual) or come with high word-error rates (automatic), and do not focus on input-conditional computation methods. To resolve these limitations, this is the first study in depression recognition task obtaining representations of both spontaneous and read speech, utilizing multimodal fusion methods, and employing Mixture of Experts (MoE) models in a single deep neural network. Specifically, we use audio files corresponding to both interview and reading tasks and convert each audio file into log-Mel spectrogram, delta, and delta-delta. Next, the image representations of the two tasks pass through shared AlexNet models. The outputs of the AlexNet models are given as input to a multimodal fusion method. The resulting vector is passed through a MoE module. In this study, we employ three variants of MoE, namely sparsely-gated MoE and multilinear MoE based on factorization. Findings suggest that our proposed approach yields an Accuracy and F1-score of 87.00% and 86.66% respectively on the Androids corpus.

翻译：抑郁是一种精神障碍，可引发心理、生理及社交等多类症状。语音已被证明是早期识别抑郁症的客观标志物，因此大量研究致力于通过语音进行抑郁识别。然而，现有方法仅使用自发性语音而忽略了朗读语音中的信息，依赖通常难以获取（人工处理）或伴随高词错误率（自动处理）的转录文本，且未关注输入条件化计算方法。为解决上述局限，本研究首次在抑郁识别任务中同时获取自发性语音与朗读语音的表征，采用多模态融合方法，并在单一深度神经网络中引入专家混合（MoE）模型。具体而言，我们使用对应访谈任务与朗读任务的音频文件，将每个音频文件转换为对数梅尔频谱图、Delta与Delta-Delta特征。随后，两个任务的图像表征通过共享的AlexNet模型处理，其输出作为多模态融合方法的输入，融合后的向量经MoE模块处理。本研究采用三种MoE变体：基于稀疏门控的MoE与基于分解的多线性MoE。实验结果表明，所提方法在Android语料库上分别达到87.00%的准确率与86.66%的F1分数。