Discrete Wavelet Transforms and Artificial Neural Networks for Speech Emotion Recognition

Automatic Emotion Recognition (AER) from speech finds greater significance in better man machine interfaces and robotics. Speech emotion based studies closely related to the databases used for the analysis. We have created and analyzed three emotional speech databases. Discrete Wavelet Transformation (DWT) was used for the feature extraction and Artificial Neural Network (ANN) was used for pattern classification. We can find that recognition accuracies vary with the type of database used. Daubechies type of mother wavelet was used for the experiment. Overall recognition accuracies of 72.05 %, 66.05%, and 71.25% could be obtained for male, female and combined male and female databases respectively.