Head Motion Analysis and Synthesis over Different Tasks

It is known that subjects vary in their head movements. This paper presents an analysis of this variety over different tasks and speakers and their impact on head motion synthesis. Measured head and articulatory movements acquired by an ElectroMagnetic Articulograph (EMA) synchronously recorded with audio was used. Data set of speech of 12 people recorded on different tasks confirms that the head motion variate over tasks and speakers. Experimental results confirmed that the proposed models were capable of learning and synthesising task-dependent head motions from speech. Subjective evaluation of synthesised head motion using task models shows that trained models on the matched task is better than mismatched one and free speech data provide models that predict preferred motion by the participants compared to read speech data.