Name
Title | ||
|---|---|---|
Acoustic feature selection and classification of emotions in speech using a 3D continuous emotion model. |
Abstract | ||
|---|---|---|
In this paper we report the results obtained from experiments with a database of emotional speech in English in order to find the most important acoustic features to estimate Emotion Primitives which determine the emotional content on speech. We are interested in exploiting the potential benefits of continuous emotion models, so in this paper we demonstrate the feasibility of applying this approach to annotation of emotional speech and we explore ways to take advantage of this kind of annotation to improve the automatic classification of basic emotions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.bspc.2011.02.008 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Automatic emotion recognition,Continuous emotion model,Feature selection | Annotation,Feature selection,Emotion classification,Speech recognition,Natural language processing,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 1 | 1746-8094 |
Citations | PageRank | References |
12 | 0.58 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Humberto Pérez-Espinosa | 1 | 16 | 1.00 |
| Carlos A. Reyes-García | 2 | 449 | 44.23 |
| Luis Villaseñor-Pineda | 3 | 403 | 53.74 |