Human-Machine interaction through audio or speech is always challenging because of the difficulties in emotion detection from audio data. Emotion is a natural way to express the individual's mental state. When a person is in an unusual mental state, the voice changes accordingly in his or her subconsciousness. Emotion detection from continuous speech with multi-lingual platform is more difficult due to complicated feature extraction and feature modeling process. Voice features change due to physical, background and channel noise or sue to other articulatory obstacles. In spite of all these uncertainties, researchers find that among glottal wave forms (consists of glottal excitation and vocal tract filters), glottal pulses carry basic information of voice because these pulses are out of articulatory effects or inner-mouth changes. In this research, we capture real-time speech signal, separate the glottal pulses and buffer the signal into specified frames to identify the phonemes those are affected mostly in emotionally unstable conditions. In these frames, we identify the basic voice controlling features like pitch, intensity, jitter to get these phonemes. For this scheme, we use a real-life continuous dataset which is a multilingual corpus of emotional speech. After feature extraction from the dataset, different classification methodologies have been applied to predict the emotional state and we find our prediction model with around 83% accuracy.

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.