Summary of the paper
| Title | A hierarchical approach with feature selection for emotion recognition from speech |
|---|---|
| Authors | Panagiotis Giannoulis and Gerasimos Potamianos |
| Abstract | We examine speaker independent emotion classification from speech, reporting experiments on the Berlin database across six basic emotions. Our approach is novel in a number of ways: First, it is hierarchical, motivated by our belief that the most suitable feature set for classification is different for each pair of emotions. Further, it uses a large number of feature sets of different types, such as prosodic, spectral, glottal flow based, and AM-FM ones. Finally, it employs a two-stage feature selection strategy to achieve discriminative dimensionality reduction. The approach results to a classification rate of 85%, comparable to the state-of-the-art on this dataset. |
| Topics | Emotion Recognition/Generation, Statistical and machine learning methods, Information Extraction, Information Retrieval |
Full paper  |
A hierarchical approach with feature selection for emotion recognition from speech |
| Bibtex | @InProceedings{GIANNOULIS12.917, author = {Panagiotis Giannoulis and Gerasimos Potamianos}, title = {A hierarchical approach with feature selection for emotion recognition from speech}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |