The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.
- MeSH
- Algorithms * MeSH
- Databases, Factual MeSH
- Emotions physiology MeSH
- Voice Quality MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Signal Processing, Computer-Assisted instrumentation MeSH
- Speech physiology MeSH
- ROC Curve MeSH
- Pattern Recognition, Automated * MeSH
- Pattern Recognition, Physiological physiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
