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Towards real-time speech emotion recognition for affective e-learning

机译：走向实时语音情感识别以进行情感电子学习

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This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILT WAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order to foster their learning. Whereas the facial emotion recognition part has been successfully tested in a previous study, the here presented study describes the development and testing of FILTWAM's vocal emotion recognition software artefact. The main goal of this study was to show the valid use of computer microphone data for real-time and adequate interpretation of vocal intonations into extracted emotional states. The software that was developed was tested in a study with 12 participants. All participants individually received the same computer-based tasks in which they were requested 80 times to mimic specific vocal expressions (960 occurrences in total). Each individual session was recorded on video. For the validation of the voice emotion recognition software artefact, two experts annotated and rated participants' recorded behaviours. Expert findings were then compared with the software recognition results and showed an overall accuracy of Kappa of 0.743. The overall accuracy of the voice emotion recognition software artefact is 67 % based on the requested emotions and the recognized emotions. Our FILTWAM-software allows to continually and unobtrusively observing learners' behaviours and transforms these behaviours into emotional states. This paves the way for unobtrusive and real-time capturing of learners' emotional states for enhancing adaptive e-learning approaches.
机译：本文介绍了FILTWAM框架的语音情感识别部分，用于情感在线学习环境中的实时情感识别。 FILT WAM（通过网络摄像头和麦克风改善学习的框架）旨在根据学习者的语调和面部表情提供及时，适当的在线反馈，以促进他们的学习。尽管在先前的研究中已经成功地测试了面部情绪识别部分，但在此提出的研究描述了FILTWAM的人声情绪识别软件人工制品的开发和测试。这项研究的主要目的是显示有效使用计算机麦克风数据来实时，适当地将语音语调解释为提取的情绪状态。开发的软件在12名参与者的研究中进行了测试。所有参与者都分别接受了相同的基于计算机的任务，其中要求他们重复80次以模仿特定的声音表达（总共960次）。每个会话都记录在视频中。为了验证语音情感识别软件伪像，两名专家对参与者的记录行为进行了注释和评分。然后将专家发现的结果与软件识别结果进行比较，得出Kappa的总体准确度为0.743。基于所请求的情绪和所识别的情绪，语音情绪识别软件人工制品的总体准确性为67％。我们的FILTWAM软件可以持续不间断地观察学习者的行为，并将这些行为转变为情绪状态。这为轻松，实时地捕获学习者的情绪状态铺平了道路，从而增强了自适应电子学习方法。

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