• Vijay Bhaskar Maske
• V V Yerigeri

The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. Research in understanding and modeling human emotions, a topic that has been predominantly dealt with in psychology and linguistics is increasingly attracting attention within the engineering community. A major motivation comes from the desire to develop human machine interfaces that are more adaptive and responsive to a user’s behavior. Capturing the emotion from only speech is a difficult challenge. The proper selection of features in relation with both the time and frequency domains together is necessary to produce optimized results. In this review paper, we concentrate on recognizing four emotional states: Happy, Sad, Anger and Neutral from speech. For this we explore features like Energy, Pitch, Zero-crossing rate and Mel-Frequency Cepstrum Coefficients (MFCC). The performance of classification algorithms, such as Support vector machine (SVMs), Decision Tree, Linear Discriminate Analysis (LDA) and etc are compared for recognizing emotions from speech in different applications.

Human-Computer Interaction (HCI), Support vector machine (SVMs) Classifier, Emotional states

"Study and analysis of speech emotion recognition system: A review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.474-477, June-2019, Available :http://www.jetir.org/papers/JETIR1906214.pdf

"Study and analysis of speech emotion recognition system: A review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp474-477, June-2019, Available at : http://www.jetir.org/papers/JETIR1906214.pdf