The Hidden Dance of Phonemes and Visage: Unveiling the Enigmatic Link between Phonemes and Facial Features

• URL: http://arxiv.org/abs/2307.13953v1
• Date: Wed, 26 Jul 2023 04:08:12 GMT
• Title: The Hidden Dance of Phonemes and Visage: Unveiling the Enigmatic Link between Phonemes and Facial Features
• Authors: Liao Qu, Xianwei Zou, Xiang Li, Yandong Wen, Rita Singh, Bhiksha Raj
• Abstract summary: This work unveils the enigmatic link between phonemes and facial features. From a physiological perspective, each segment of speech -- phoneme -- corresponds to different types of airflow and movements in the face. Our results indicate that AMs are more predictable from vowels compared to consonants, particularly with plosives.
• Score: 27.89284938655708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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