Paper Info
Title
Benchmarking classification models for emotion recognition in natural speech: A multi-corporal study
Abstract
A significant amount of the research on automatic emotion recognition from speech focuses on acted speech that is produced by professional actors. This approach often leads to overoptimistic results as the recognition of emotion in real-life conditions is more challenging due the propensity of mixed and less intense emotions in natural speech. The paper presents an empirical study of the most widely used classifiers in the domain of emotion recognition from speech, across multiple non-acted emotional speech corpora. The results indicate that Support Vector Machines have the best performance and that they along with Multi-Layer Perceptron networks and k-nearest neighbour classifiers perform significantly better (using the appropriate statistical tests) than decision trees, Naive Bayes classifiers and Radial Basis Function networks.
Year
DOI
Venue
2011
10.1109/FG.2011.5771359
FG
Keywords
Field
DocType
statistical test,classification,decision trees,emotion,speech,empirical study,decision tree,radial basis function network,speech recognition,support vector machine,kernel,niobium,support vector machines
Kernel (linear algebra),Decision tree,Naive Bayes classifier,Computer science,Support vector machine,Speech recognition,Artificial intelligence,Perceptron,Empirical research,Benchmarking,Statistical hypothesis testing,Machine learning
Conference
Citations 
PageRank 
References 
6
0.45
19
Authors
2
Name
Order
Citations
PageRank
Alexey Tarasov1283.54
Sarah Jane Delany244629.95