Speaker Independent Automatic Emotion Recognition from Speech: A Comparison of MFCCs and Discrete Wavelet Transforms

Automatic Emotion Recognition (AER) from speech is one of the most interested research domains for the scientific world. AER simply means to make a machine able to recognize the different emotions from speech. We have created and analyzed an elicited database consisting of 700 utterances under four different emotional classes such as neutral happy sad and anger. Malayalam (One of the south Indian languages) was used to conduct the experiment. We have used a database proportion of 80:20 for the training and testing purpose. We have analyzed the emotional speech corpus recorded by using both Discrete Wavelet Transforms (DWTs) and Mel Frequency Cepstral Coefficients (MFCCs) and obtained an overall recognition accuracy of 68.5% and 55% respectively. artificial neural network was used for the classification and recognition purpose.