700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ ReadersThis Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Research Article Open Access
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.
To read the full article Peer-reviewed Article PDF
Author(s): Ritu D.Shah , Dr. Anil.C.Suthar
Support vector Machine, Mel Frequency Cepstral Coefficients, Speech signal, Emotion recognition., Speech Therapy for Adults,Speech and Language pathology,Bilingual Speech pathology,Speech and Language Disorders.