Emotion Recognition for Instantaneous Marathi Spoken Words

This paper explore on emotion recognition from Marathi speech signals by using feature extraction techniques and classifier to classify Marathi speech utterances according to their emotional contains. A different type of speech feature vectors contains different emotions, due to their corresponding natures. In this we have categorized the emotions as namely Anger, Happy, Sad, Fear, Neutral and Surprise. Mel Frequency Cepstral Coefficient (MFCC) feature parameters extracted from Marathi speech Signals depend on speaker, spoken word as well as emotion. Gaussian mixture Models (GMM) is used to develop Emotion classification model. In this, recently proposed feature extraction technique and classifier is used for Marathi spoken words. In this each subject/Speaker has spoken 7 Marathi words with 6 different emotions that 7 Marathi words are Aathawan, Aayusha, Chamakdar, Iishara, Manav, Namaskar, and Uupay. For experimental work we have created total 924 Marathi speech utterances database and from this we achieved the empirical performance of overall emotion recognition accuracy rate obtained using MFCC and GMM is 84.61% rate of our Emotion Recognition for Marathi Spoken Words (ERFMSW) system. We got average accuracy for male and female is 86.20% and 83.03% respectively.