Descriptive analysis of emotion and feeling in voice

M. Shimura, Fumiaki Monma, S. Mitsuyoshi, M. Shuzo, Taishi Yamamoto, I. Yamada
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引用次数: 6

Abstract

Recognition of human “emotions” or “feelings” from voice is important to research on human communications. Although there has been much research on emotions or feelings in voice, definitions of these terms have been inconsistent. We reviewed previous papers in linguistics, brain science, information science, etc. and developed specific definitions for these term. In our paper, “emotion” is defined as an involuntary reaction in the human brain; it has two states: pleasure and displeasure. “Feeling” (e.g., anger, enjoyment, sadness, fear, and distress) is defined as a state voluntarily resulting from an emotion. Here, we should notice that the pleasure-displeasure direction does not always correspond to the feeling. So, our objective is to obtain sufficient amount of voice data and to analyze the relationship between emotions and feelings. In voice recording experiments, the voice database from about 100 participants with various natural feelings was constructed. A result of descriptive analysis showed that pleasure-displeasure direction did not correspond to the each feeling in 5% of voice data. This result suggested that, if an experimental situation is constructed that tends to arouse various feelings, data with less variability can be obtained. Further analysis of the characteristics of the data obtained to identify situations in which the pleasure-displeasure direction does not necessarily correspond to the basic feeling should lead to improved accuracy of voice emotion recognition.
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声音中情绪和感觉的描述性分析
从声音中识别人的“情绪”或“感觉”,对研究人类交流具有重要意义。尽管对声音中的情绪或感觉进行了很多研究,但这些术语的定义一直不一致。我们回顾了语言学、脑科学、信息科学等方面的文献,并对这些术语进行了具体的定义。在我们的论文中,“情感”被定义为人类大脑中的一种无意识反应;它有两种状态:快乐和不快乐。“感觉”(如愤怒、享受、悲伤、恐惧和痛苦)被定义为一种由情绪自发产生的状态。在这里,我们应该注意到快乐-不快乐的方向并不总是与感觉相对应。因此,我们的目标是获得足够数量的语音数据,并分析情绪和感觉之间的关系。在录音实验中,构建了约100名具有各种自然感受的参与者的语音数据库。描述性分析的结果表明,在5%的语音数据中,快乐-不快乐的方向与每种感觉不对应。这一结果表明,如果构建一个易于引起各种感受的实验情境,则可以获得变异性较小的数据。进一步分析所获得的数据的特征,以确定快乐-不快乐方向不一定与基本感觉相对应的情况,这将提高语音情绪识别的准确性。
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