论文标题
将文本和基于图的功能集成用于检测语音心理健康障碍的功能
Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice
论文作者
论文摘要
借助智能手机等语音设备的可用性，可以先前检测和治疗心理健康障碍，尤其是大流行。当前方法涉及直接从音频信号提取功能。在本文中，使用两种方法来丰富语音分析以进行抑郁检测：语音信号的图形转换，以及基于代表性学习的成绩单的自然语言处理，融合在一起以产生最终类标签。使用DAIC-WOZ数据集实验的结果表明，基于文本的语音分类和从低级别和基于图的语音信号特征学习的整合可以改善抑郁等精神障碍的检测。
With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic. The current methods involve extracting features directly from audio signals. In this paper, two methods are used to enrich voice analysis for depression detection: graph transformation of voice signals, and natural language processing of the transcript based on representational learning, fused together to produce final class labels. The results of experiments with the DAIC-WOZ dataset suggest that integration of text-based voice classification and learning from low level and graph-based voice signal features can improve the detection of mental disorders like depression.
