Finding Emotion from Multi-lingual Voice Data

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.