首页> 外文会议>COST 2102 International Training School on Towards Autonomous, Adaptive, and Context-Aware Multimodal Interfaces >Problems of the Automatic Emotion Recognitions in Spontaneous Speech; An Example for the Recognition in a Dispatcher Center
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Problems of the Automatic Emotion Recognitions in Spontaneous Speech; An Example for the Recognition in a Dispatcher Center

机译:自动言论中自动情感识别的问题;调度中心识别的示例

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Numerous difficulties, in the examination of emotions occurring in continuous spontaneous speech, are discussed in this paper, than different emotion recognition experiments are presented, using clauses as the recognition unit. In a testing experiment it was examined that what kind of acoustical features are the most important for the characterization of emotions, using spontaneous speech database. An SVM classifier was built for the classification of 4 most frequent emotions. It was found that fundamental frequency, energy, and its dynamics in a clause are the main characteristic parameters for the emotions, and the average spectral information, as MFCC and harmonicity are also very important. In a real life experiment automatic recognition system was prepared for a telecommunication call center. Summing up the results of these experiments, we can say, that clauses can be an optimal unit of the recognition of emotions in continuous speech.
机译:在持续自发性言论中发生的情绪的检查中,在本文中讨论了许多困难,这些论文讨论了不同的情感识别实验,使用子句作为识别单元。在测试实验中,检查了使用自发语音数据库的情绪表征的最重要的一种声学特征。 SVM分类器是为4个最常见情绪的分类而建立的。结果发现,基本频率,能量及其动力学是情绪的主要特征参数,以及平均光谱信息,因为MFCC和谐波也非常重要。在真实的实验中,实验实验为电信呼叫中心准备了自动识别系统。总结这些实验的结果,我们可以说,这些条款可以是持续演讲中情绪的最佳单位。

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