NDLI: Exploring dataset similarities using PCA-based feature selection

Content Provider	IEEE Xplore Digital Library
Author	Siegert, I. Bock, R. Wendemuth, A. Vlasenko, B.
Copyright Year	2015
Description	Author affiliation: Idiap Res. Inst., Martigny, Switzerland (Vlasenko, B.) \|\| Cognitive Syst. Group, Otto von Guericke Univ., Magdeburg, Germany (Siegert, I.; Bock, R.; Wendemuth, A.)
Abstract	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.
Starting Page	387
Ending Page	393
File Size	827125
Page Count	7
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344600
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cross-Corpus Emotion Recognition Databases Speech recognition Speech Feature extraction Noise measurement Stress Principal component analysis PCA
Content Type	Text
Resource Type	Article

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