Name
Abstract | ||
|---|---|---|
Emotion recognition grows to an important factor in future media retrieval and man machine interfaces. However, even human deciders often experience problems realizing one's emotion, especially of strangers. In this work we strive to recognize emotion independent of the person concentrating on the speech channel. Single feature relevance of acoustic features is a critical point, which we address by filter-based gain ratio calculation starting at a basis of 276 features. As optimization of a minimum set as a whole in general saves more extraction effort, we furthermore apply an SVM-SFFS wrapper based search. For a more robust estimation we also integrate spoken content information by a Bayesian Net analysis of ASR Outputs. Overall classification is realized in an early feature fusion by stacked ensembles of diverse base classifiers. Tests ran on a 3,947 movie and automotive interaction dialog-turns database consisting of 35 speakers. Remarkable overall performance can be reported in the discrimination of the seven discrete emotions named in the MPEG-4 standard with added neutrality. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICME.2005.1521560 | 2005 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), VOLS 1 AND 2 |
Keywords | Field | DocType |
bayesian methods,information analysis,robustness,support vector machine,optimization,testing,data mining,speech recognition,man machine interface,support vector machines,automatic speech recognition,robust estimator,critical point,data compression | Pattern recognition,Emotion recognition,Computer science,Support vector machine,Communication channel,Robustness (computer science),Speech recognition,Artificial intelligence,Information gain ratio,Data compression,Automotive industry,Bayesian probability | Conference |
Citations | PageRank | References |
39 | 2.26 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Björn Schuller | 1 | 6749 | 463.50 |
| Stephan Reiter | 2 | 278 | 17.21 |
| Ronald Müller | 3 | 174 | 11.03 |
| Marc Al-Hames | 4 | 116 | 8.75 |
| Manfred K. Lang | 5 | 141 | 11.94 |
| Gerhard Rigoll | 6 | 2788 | 268.87 |