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

arXiv: https://arxiv.org/abs/2307.13953 · HTML: https://mlsanity.com/paper/2307.13953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Speech2Face: Learning the Face Behind a Voice](https://mlsanity.com/paper/1905.09773/) (2019)
- [Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech](https://mlsanity.com/paper/1903.12424/) (2019)
- [From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech](https://mlsanity.com/paper/2004.05830/) (2020)
- [Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?](https://mlsanity.com/paper/2203.09824/) (2022)
- [Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices](https://mlsanity.com/paper/2104.10299/) (2021)

## Abstract (arXiv)

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.