Efficient and effective strategies for cross-corpus acoustic emotion recognition
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Dosyalar
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
An important research direction in speech technology is robust cross-corpus and cross-language emotion recognition. In this paper, we propose computationally efficient and performance effective feature normalization strategies for the challenging task of cross-corpus acoustic emotion recognition. We particularly deploy a cascaded normalization approach, combining linear speaker level, nonlinear value level and feature vector level normalization to minimize speaker-and corpus-related effects as well as to maximize class separability with linear kernel classifiers. We use extreme learning machine classifiers on five corpora representing five languages from different families, namely Danish, English, German, Russian and Turkish. Using a standard set of suprasegmental features, the proposed normalization strategies show superior performance compared to benchmark normalization approaches commonly used in the literature. (C) 2017 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Extreme learning machines, Acoustic emotion recognition, Cross-corpus adaptation, Extreme Learning-Machine, Physical Load, Challenge, Classification, Networks
Kaynak
Neurocomputing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
275
