Fractal Antenna Design using HFSS
The project presents speech emotion recognition from speech signals based on features analysis and NN-classifier. Automatic speech emotion recognition (SER) plays an important role in HCI systems for measuring people’s emotions and has dominated psychology by linking expressions to groups of basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). The recognition system involves speech emotion detection, feature extraction and selection, and finally classification. These features are useful to distinguish the maximum number of samples accurately and the NN classifier based on discriminant analysis is used to classify the six different expressions. The simulated results will be shown that the filter-based feature extraction with the used classifier gives much better accuracy with lesser algorithmic complexity than other speech emotion expression recognition approaches.Fractal Antenna Design using HFSS
- Principal Component Analysis
- Geometric methods.
- Low discriminatory power and high computational load
- In geometric-based methods, the geometric features like the distance between speech signals.
Speech Emotion recognition for transform features system through textural analysis and NN classifier. The system involves,
- Speech Signal
- Features Extraction using mfcc
- NN Classifier
- Robustness to illumination changes
- Low complexity
- High discriminatory power
- Border security system
- Robotics and Computer games
- Machine vision
- Python idle above 3.0
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 Y. Sun and G. Wen, “Emotion recognition using semi-supervised feature selection with speaker normalization,” Springer-Verlag New York, Inc., September 2015.