Name
Abstract | ||
|---|---|---|
This work aims at investigating the use of relevance vector machine (RVM) for speech emotion recognition. The RVM technique is a Bayesian extension of the support vector machine (SVM) that is based on a Bayesian formulation of a linear model with an appropriate prior for each weight. Together with the introduction of RVM, aspects related to the use of SVM are also presented. From the comparison between the two classifiers, we find that RVM achieves comparable results to SVM, while using a sparser representation, such that it can be advantageously used for speech emotion recognition. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24571-8_12 | ACII (2) |
Keywords | Field | DocType |
support vector machine,sparser representation,bayesian extension,comparable result,linear model,bayesian formulation,relevance vector machine,speech emotion recognition,rvm technique | Feature selection,Linear model,Emotion recognition,Computer science,Support vector machine,Speech recognition,Artificial intelligence,Relevance vector machine,Bayesian formulation,Machine learning,Bayesian probability | Conference |
Volume | ISSN | Citations |
6975 | 0302-9743 | 8 |
PageRank | References | Authors |
0.49 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengna Wang | 1 | 41 | 2.94 |
| Werner Verhelst | 2 | 431 | 51.55 |
| Hichem Sahli | 3 | 475 | 65.19 |