• R. Bhuvaneswari
• S. Tamilarasi

In this paper methodology for emotion recognition from speech signal is presented. Here, some of acoustic features are extracted from speech signal to analyze the characteristics and behavior of speech. The system is used to recognize the basic emotions: Anger, Happiness, Sadness and Neutral. It can serve as a basis for further designing an application for human like interaction with machines through natural language processing and improving the efficiency of emotion. In this, formant, energy, Mel Frequency Cepstral Coefficients (MFCC) has been used for feature extraction from the speech signal. Support Vector Machine (SVM) are used for recognition of emotional states. English datasets are used for analysis of emotions with SVM Kernel functions. Using this analysis the machine is trained and designed for detecting emotions in real time speech.

Speech Emotion Recognition Based on SVM Using python

"Speech Emotion Recognition Based on SVM Using python", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.e158-e165, March-2023, Available :http://www.jetir.org/papers/JETIR2303423.pdf

"Speech Emotion Recognition Based on SVM Using python", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppe158-e165, March-2023, Available at : http://www.jetir.org/papers/JETIR2303423.pdf