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.

本研究揭示了音位和面部特征之间的神秘联系，通过提出细粒度的语音 - 面部关系分析流程，证实了元音比辅音更容易通过行气和面部运动来预测面部人体测量学属性，并为未来研究语音 - 面部多模式学习奠定了基础。

音素和面貌的隐秘舞蹈：揭示音素与面部特征之间的神秘关联

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

Multiple studies in the past have shown that there is a strong correlation
between human vocal characteristics and facial features. However, existing
approaches generate faces simply from voice, without exploring the set of
features that contribute to these observed correlations. A computational
methodology to explore this can be devised by rephrasing the question to: "how
much would a target face have to change in order to be perceived as the
originator of a source voice?" With this in perspective, we propose a framework
to morph a target face in response to a given voice in a way that facial
features are implicitly guided by learned voice-face correlation in this paper.
Our framework includes a guided autoencoder that converts one face to another,
controlled by a unique model-conditioning component called a gating controller
which modifies the reconstructed face based on input voice recordings. We
evaluate the framework on VoxCelab and VGGFace datasets through human subjects
and face retrieval. Various experiments demonstrate the effectiveness of our
proposed model.

本研究提出了一种基于先前学习到的语音 - 面部相关性的引导自编码器框架，通过将目标面孔形态学变换来匹配语音信息，显著提高了人脸检索和识别的准确性。