Real-time emotion recognition from speech using echo state networks

被引：0

作者：

Scherer, Stefan ^{[1
]}

Oubbati, Mohamed ^{[1
]}

Schwenker, Friedhelm ^{[1
]}

Palm, Guenther ^{[1
]}

机构：

[1] Univ Ulm, Inst Neural Informat Proc, D-89069 Ulm, Germany

来源：

ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION, PROCEEDINGS | 2008年 / 5064卷

关键词：

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：

The goal of this work is to investigate real-time emotion recognition in noisy environments. Our approach is to solve this problem using novel recurrent neural networks called echo state networks (ESN). ESNs utilizing the sequential characteristics of biologically motivated modulation spectrum features are easy to train and robust towards noisy real world conditions. The standard Berlin Database of Emotional Speech is used to evaluate the performance of the proposed approach. The experiments reveal promising results overcoming known difficulties and drawbacks of common approaches.

引用

页码：205 / 216

页数：12

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