  This work unveils the enigmatic link between phonemes and facial features.
Traditional studies on voice-face correlations typically involve using a long
period of voice input, including generating face images from voices and
reconstructing 3D face meshes from voices. However, in situations like
voice-based crimes, the available voice evidence may be short and limited.
Additionally, from a physiological perspective, each segment of speech --
phoneme -- corresponds to different types of airflow and movements in the face.
Therefore, it is advantageous to discover the hidden link between phonemes and
face attributes. In this paper, we propose an analysis pipeline to help us
explore the voice-face relationship in a fine-grained manner, i.e., phonemes
v.s. facial anthropometric measurements (AM). We build an estimator for each
phoneme-AM pair and evaluate the correlation through hypothesis testing. Our
results indicate that AMs are more predictable from vowels compared to
consonants, particularly with plosives. Additionally, we observe that if a
specific AM exhibits more movement during phoneme pronunciation, it is more
predictable. Our findings support those in physiology regarding correlation and
lay the groundwork for future research on speech-face multimodal learning.
