# Phoneme-to-viseme mappings: the good, the bad, and the ugly

arXiv: https://arxiv.org/abs/1805.02934 · HTML: https://mlsanity.com/paper/1805.02934/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 7 of 7)

- [DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization](https://mlsanity.com/paper/2411.10193/) (2024)
- [Perceptually Accurate 3D Talking Head Generation: New Definitions, Speech-Mesh Representation, and Evaluation Metrics](https://mlsanity.com/paper/2503.20308/) (2025)
- [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023)
- [Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction](https://mlsanity.com/paper/2404.12725/) (2024)
- [UniSync: A Unified Framework for Audio-Visual Synchronization](https://mlsanity.com/paper/2503.16357/) (2025)
- [Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation](https://mlsanity.com/paper/2504.18539/) (2025)
- [Curriculum-Based Noise Adaptation for Phoneme-to-Text Reconstruction in Visual Speech Recognition](https://mlsanity.com/paper/2609.20839/) (2026)

## Abstract (arXiv)

Visemes are the visual equivalent of phonemes. Although not precisely defined, a working definition of a viseme is "a set of phonemes which have identical appearance on the lips". Therefore a phoneme falls into one viseme class but a viseme may represent many phonemes: a many to one mapping. This mapping introduces ambiguity between phonemes when using viseme classifiers. Not only is this ambiguity damaging to the performance of audio-visual classifiers operating on real expressive speech, there is also considerable choice between possible mappings. In this paper we explore the issue of this choice of viseme-to-phoneme map. We show that there is definite difference in performance between viseme-to-phoneme mappings and explore why some maps appear to work better than others. We also devise a new algorithm for constructing phoneme-to-viseme mappings from labeled speech data. These new visemes, `Bear' visemes, are shown to perform better than previously known units.