Emotion sensing or detection is nowadays vital research area since it has many applications in mental-health recognition based technology, biometric security analysis, etc. It is a challenging research area because voice features can vary based on gender, physical or mental condition and environmental noise. In our research, we provide a novel framework for emotion detection based on jitter computing. Here, rather than using the entire voice signal, we use short time significant frames, which would be enough to identify the emotional condition of the speaker. This makes our framework less costly. We collect data set from real users and apply our method. We compare our method with other popular methods and we find that our method provides better accuracy, true acceptance rate, less error rate.
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