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Supervisor: Michael Jenkin

Contact: jenkin@eecs.yorku.ca

When a robot interacts with a human it can be very helpful if the robot can estimate the emotional state of the person to whom it is talking. This estimation can be made in a number of ways, from the choice of words used, the visual cues obtained from a camera pointed at the person, and through an analysis if the speech signal obtained from a microphone listening to the speaker. This project is concerned with this last task. This project will apply modern recurrent neural networks, and in particular LSTM to the problem. The project will involve three major phases

(i) Background work: Understanding the problem of estimating speech emotion from the audio signal and a review of approaches to date. Identifying and obtaining access to appropriate datasets for training and evaluations.

(ii) Basic implementation: Based on the results of above, implement an existing LSTM-based approach to the problem and evaluate on the dataset identified above.

(iii) Integration with a social robot system (SENTRYNet) so that the robot can utilize this information in the development of dialog when interacting with speakers.

Required skills: General EECS408x prerequisites, Good knowledge of Python. Ability to work independently.