Analysis of all pole model to recognize emotions from speech signal

Speech is the vocalized form of communication. Speech is articulated by moving the tongue, lips, lower jaw to shape the voiced or unvoiced airflow. Emotion recognition results from speech signals, focusing on the extraction of emotion features. The intention of a spoken utterance can be affected by the emotions. In human-computer or human-human interaction systems, emotion recognition systems could provide users with improved services by being adaptive to their emotions. This paper aims to compute the Denominator (AR), Gain (G), Reflection (R) coefficients of All Pole Model and then recognize the emotions from the speech. The important steps followed are Pitch Estimation, Feature Extraction, Statistical Analysis, Classification of the emotions from the speech signal. The speech signal is obtained from the wave file. A single sentence is considered and the Pitch of the sentence can be identified by the Pitch Marker Algorithm. The statistical Classification of the emotions can be done by the Discriminant Analysis and K-NN classifiers to achieve a better success rate. The results show that the negative emotions Anger, Sad, Boredom, Disgust achieved a higher recognition rate than the other emotions. The emotion Sad attained the maximum recognition rate of 100% with the GAIN coefficients under the MDA classification. The database used here is the Berlin Database.