# Which phoneme-to-viseme maps best improve visual-only computer lip-reading?

arXiv: https://arxiv.org/abs/1710.01093 · HTML: https://mlsanity.com/paper/1710.01093/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [VALLR: Visual ASR Language Model for Lip Reading](https://mlsanity.com/paper/2503.21408/) (2025)
- [Enhancing Portuguese Sign Language Animation with Dynamic Timing and Mouthing](https://mlsanity.com/paper/2307.06124/) (2023)
- [Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition](https://mlsanity.com/paper/2402.13004/) (2024)
- [Evaluation of End-to-End Continuous Spanish Lipreading in Different Data Conditions](https://mlsanity.com/paper/2502.00464/) (2025)

## Abstract (arXiv)

A critical assumption of all current visual speech recognition systems is that there are visual speech units called visemes which can be mapped to units of acoustic speech, the phonemes. Despite there being a number of published maps it is infrequent to see the effectiveness of these tested, particularly on visual-only lip-reading (many works use audio-visual speech). Here we examine 120 mappings and consider if any are stable across talkers. We show a method for devising maps based on phoneme confusions from an automated lip-reading system, and we present new mappings that show improvements for individual talkers.