Towards an online voice-based gender and internal state detection model

Abstract

In human-robot interaction, gender and internal state detection play an important role in making the robot reacting in an appropriate manner. This research focuses on the important features to extract from a voice signal in order to construct successful gender and internal state detection systems, and shows the benefits of combining both systems together on the total average recognition score. Moreover, it consists a foundation on an ongoing approach to estimate the human internal state online via unsupervised clustering algorithms.