Exploring Dataset Similarities using PCA-based Feature Selection

被引：0
|
作者：
Siegert, Ingo [1 ]
Boeck, Ronald [1 ]
Wendemuth, Andreas [1 ]
Vlasenko, Bogdan [2 ]
机构：
[1] Univ Magdeburg, Cognit Syst Grp, D-39106 Magdeburg, Germany
[2] Idiap Res Inst, Martigny, Switzerland
来源：
2015 INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII) | 2015年
关键词：
PCA; Cross-Corpus Emotion Recognition; SPEECH; RECOGNITION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In emotion recognition from speech, several well established corpora are used to date for the development of classification engines. The data is annotated differently, and the community in the field uses a variety of feature extraction schemes. The aim of this paper is to investigate promising features for individual corpora and then compare the results for proposing optimal features across data sets, introducing a new ranking method. Further, this enables us to present a method for automatic identification of groups of corpora with similar characteristics. This answers an urgent question in classifier development, namely whether data from different corpora is similar enough to jointly be used as training material, overcoming shortage of material in matching domains. We compare the results of this method with manual groupings of corpora. We consider the established emotional speech corpora AVIC, ABC, DES, EMO-DB, ENTERFACE, SAL, SMARTKOM, SUSAS and VAM, however our approach is general.
引用
收藏
页码：387 / 393
页数：7
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