基于发音协调特征和层次注意文本嵌入的多模态抑郁分类
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings
摘要：
近年来，多模式抑郁症分类得到了极大的普及。我们开发了一个多模态抑郁症分类系统，使用发音协调功能提取声道变量和文本transmittance从自动语音识别工具，产生改善的接收器操作特征曲线下的面积相比，单峰分类（7.5%和13.7%的音频和文本分别）。我们表明，在训练数据有限的情况下，可以首先训练分段级分类器，然后使用多级卷积递归神经网络在不影响性能的情况下获得会话预测。使用分层注意力网络（HAN）训练文本模型。多模态系统是通过将会话级音频模型和HAN文本模型的嵌入相结合来开发的
英文摘要：
Multimodal depression classification has gained immense popularity over the recent years. We develop a multimodal depression classification system using articulatory coordination features extracted from vocal tract variables and text transcriptions obtained from an automatic speech recognition tool that yields improvements of area under the receiver operating characteristics curve compared to uni-modal classifiers (7.5% and 13.7% for audio and text respectively). We show that in the case of limited training data, a segment-level classifier can first be trained to then obtain a session-wise prediction without hindering the performance, using a multi-stage convolutional recurrent neural network. A text model is trained using a Hierarchical Attention Network (HAN). The multimodal system is developed by combining embeddings from the session-level audio model and the HAN text model
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
