论文标题
使用神经网络和信息可视化基于语音的情绪识别
Speech-Based Emotion Recognition using Neural Networks and Information Visualization
论文作者
论文摘要
情绪识别通常用于健康评估。但是，用于治疗评估的典型指标是基于患者服用者的评估。这个过程可能涉及主观性问题，同时还要求医疗保健专业人员处理大量信息。因此，机器学习算法可能是对情绪分类的有用工具。尽管该领域已经开发了几种模型，但缺乏对治疗情绪分类系统的用户友好表示。我们提出了一种工具，该工具使用户能够通过机器学习模型从音频元素中识别出一系列情感（快乐，悲伤，愤怒，惊讶，中立，蛤，厌恶和恐惧）。该仪表板是根据当地治疗师对语音数据直观表示的需求而设计的，以便获得与患者会议的见解和信息分析。
Emotions recognition is commonly employed for health assessment. However, the typical metric for evaluation in therapy is based on patient-doctor appraisal. This process can fall into the issue of subjectivity, while also requiring healthcare professionals to deal with copious amounts of information. Thus, machine learning algorithms can be a useful tool for the classification of emotions. While several models have been developed in this domain, there is a lack of userfriendly representations of the emotion classification systems for therapy. We propose a tool which enables users to take speech samples and identify a range of emotions (happy, sad, angry, surprised, neutral, clam, disgust, and fear) from audio elements through a machine learning model. The dashboard is designed based on local therapists' needs for intuitive representations of speech data in order to gain insights and informative analyses of their sessions with their patients.
