论文标题
使用关节协调功能和基于分层注意的文本嵌入的多模式抑郁分类
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings
论文作者
论文摘要
近年来，多模式抑郁症分类已获得巨大的流行。我们使用从自动语音识别工具中获得的发音协调特征开发了多模式抑郁分类系统，该功能从自动语音识别工具中获得，该工具与Uni-Modal-Modal Classifier相比（分别为Audio和文本分别为7.5％和13.7％）获得了接收器操作特征曲线下面积下的面积的改进）。我们表明，在有限的培训数据的情况下，可以首先对细分级分类器进行培训，然后使用多阶段卷积复发性神经网络获得会话的预测而不会阻碍性能。使用分层注意网络（HAN）训练文本模型。多模式系统是通过结合会话级音频模型和HAN文本模型的嵌入来开发的
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
