Quantification of Segmentation and F0 Errors and Their Effect on Emotion Recognition

被引:0
|
作者
Steidl, Stefan [1 ]
Batliner, Anton [1 ]
Noeth, Elinar [1 ]
Hornegger, Joachim [1 ]
机构
[1] Univ Erlangen Nurnberg, Lehrstuhl Mustererkennung, D-91058 Erlangen, Germany
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Prosodic features modelling pitch. energy, and duration play a major role in speech emotion recognition. Our word level features. especially duration and pitch features. rely oil correct word segmentation and F0 extraction. For the FAU Aibo Emotion Corpus, the automatic segmentation of a forced alignment of the spoken word Sequence and the automatically extracted F0 Values have been manually corrected. Frequencies of different types of segmentation and F0errors are given and their influence on emotion recognition using different groups of prosodic features is evaluated. The classification results show that the impact of these errors oil emotion recognition is small.
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页码:525 / 534
页数:10
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