Published Paper Details:

Emotional speech signals research has been boosted in human-machine interfaces due to the availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. The human voice is very versatile and carries a multitude of emotions. Emotion in speech carries extra insight into human actions. Through further analysis, we can better understand the motives of people, whether they are unhappy customers or cheering fans. Humans are easily able to determine the emotion of a speaker, but the field of emotion recognition through machine learning is an open research area. In this proposed project, we perform speech data analysis on speaker discriminated speech signals to detect the emotions of the individual speakers involved in the conversation. We are analyzing different techniques to perform speaker discrimination and speech analysis to find efficient algorithms to perform this task.