A machine learning application for emotion recognition from speech.
Language: Python 2.7
Mario Ruggieri
E-mail: [email protected]
- pyAudioAnalysis for short time features extraction
- scikit-learn for preprocessing, classification and validation
Download Berlin DB from the link. Request DaFeX dataset following the link instructions. The code will generate automatically .wav files
| Long option | Option | Description |
|---|---|---|
| --dataset | -d | dataset type |
| --dataset_path | -p | dataset path |
| --load_data | -l | load dataset data and info and save them into a .p file |
| --extract_features | -e | extract features from data and save them into a .p file |
| --speaker_indipendence | -s | cross validation is made using different actors for train and test sets |
| --plot_eigenspectrum | -i | show eigenspectrum for each training set |
Example:
python emorecognition.py -d 'berlin' -p [berlin db path] -e -l
The first time you run the application, -l and -e options are mandatory because you need to extract data and features. Every time you change the feature extraction method and/or the dataset data you need to specify -e and/or -l to update your .p files.
Please read LICENSE file.
- [1] Burkhardt F., Paeschke A., Rolfes M., Sendlmeier W. and Weiss B., A Database of German Emotional Speech, Proceedings Interspeech 2005, Lissabon, Portugal
- [2] Battocchi, A.; Pianesi, F.; Goren-Bar, D.. A First Evaluation Study of a Database of Kinetic Facial Expressions (DaFEx). Proceedings of the 7th International Conference on Multimodal Interfaces ICMI 2005, October 04-06, 2005, Trento (Italy), pp. 214- 221. ACM Press New York, NY, USA.
- [3] Battocchi, A.; Pianesi, F.; Goren-Bar, D.. The Properties of DaFEx, a Database of Kinetic Facial Expressions. In Jianhua Tao, Tieniu Tan, Rosalind W. Picard (Eds.): Affective Computing and Intelligent Interaction, First International Conference, ACII 2005, Beijing, China, October 22-24, 2005, Proceedings. Lecture Notes in Computer Science 3784 Springer 2005, pp. 558-565.