情绪识别
计算机科学
分类器(UML)
唤醒
价(化学)
情绪分类
人工智能
领域(数学)
活动识别
模式识别(心理学)
语音识别
情感计算
人机交互
心理学
数学
量子力学
物理
神经科学
纯数学
作者
Andreas Haag,Silke Goronzy,Peter Schaich,Jason D. Williams
标识
DOI:10.1007/978-3-540-24842-2_4
摘要
The detection of emotion is becoming an increasingly important field for human-computer interaction as the advantages emotion recognition offer become more apparent and realisable. Emotion recognition can be achieved by a number of methods, one of which is through the use of bio-sensors. Bio-sensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6% and 89.9% for recognition of emotion arousal and valence respectively.
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