An Evaluation Model For Speech Driven Gesture Synthesis
Abstract
The research and development of embodied agents with advanced relational capabilities is constantly evolving. In recent years, the development of be- havioural signal generation models to be integrated in social robots and vir- tual characters, is moving from rule-based to data-driven approaches, re- quiring appropriate and reliable evaluation techniques. This work proposes a novel machine learning approach for the evaluation of speech-to-gestures models that is independent from the audio source. This approach enables the measurement of the quality of gestures produced by these models and provides a benchmark for their evaluation. Results show that the proposed approach is consistent with evaluations made through user studies and, fur- thermore, that its use allows for a reliable comparison of speech-to-gestures state-of-the-art models.