Emotion recognition and speaker verification using machine learning

The aim of the work is to develop machine learning algorithms that can verify the identity of a speaker and infer their emotional state. Emotion recognition utilizes a person’s voice characteristics to infer their emotional state and has been used for several applications including quality metrics on call centres, determining patients feeling etc. In a different vein speaker verification utilizes voice extracted features to properly identify individuals and has been used for several applications including security systems, authenticating speakers, etc. State-of-the-art approaches in both fields are achieved through deep learning techniques. Deep learning models trained in an end-to-end fashion do not require the guidance from experts to achieve satisfactory performance in their tasks. A rich dataset of examples depicting the desired results is enough to allow the models to generalize well to unobserved scenarios.

Basic knowledge of machine learning process and python programming language.

Good