Low-Level Characterization of Expressive Head Motion Through Frequency Domain Analysis

Abstract

For the purpose of understanding how head motions contribute to the perception of emotion in an utterance, we aim to examine the perception of emotion based on Fourier transform-based static and dynamic features of head motion. Our work is to conduct intra-related objective analysis and perceptual experiments on the link between the perception of emotion and the static/dynamic features. The objective analysis outcome shows that the static and dynamic features are effective in characterizing and recognizing emotions. The perceptual experiments enable us to collect human perception of emotion through head motion. The collected perceptual data shows that humans are unable to reliably perceive emotion from head motion alone but reveals that humans are sensitive to the static feature (in reference to the averaged up-down rotation angle) and the dynamic features (which reflect the fluidity and speed of movement). It also indicates that humans perceive emotion carried in head motion and the naturalness of head motion in two different channels. Our work contributes to the understanding and the characterization of head motion in expressive speech through low-level descriptions of motion features, instead of commonly used high-level motion style (e.g., head nods, shakes, tilts, and raises).