# The speaker-independent lipreading play-off; a survey of lipreading machines

arXiv: https://arxiv.org/abs/1810.10597 · HTML: https://mlsanity.com/paper/1810.10597/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning](https://mlsanity.com/paper/2202.13403/) (2022)
- [Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization](https://mlsanity.com/paper/2403.16071/) (2024)
- [Advances and Challenges in Deep Lip Reading](https://mlsanity.com/paper/2110.07879/) (2021)

## Abstract (arXiv)

Lipreading is a difficult gesture classification task. One problem in computer lipreading is speaker-independence. Speaker-independence means to achieve the same accuracy on test speakers not included in the training set as speakers within the training set. Current literature is limited on speaker-independent lipreading, the few independent test speaker accuracy scores are usually aggregated within dependent test speaker accuracies for an averaged performance. This leads to unclear independent results. Here we undertake a systematic survey of experiments with the TCD-TIMIT dataset using both conventional approaches and deep learning methods to provide a series of wholly speaker-independent benchmarks and show that the best speaker-independent machine scores 69.58% accuracy with CNN features and an SVM classifier. This is less than state of the art speaker-dependent lipreading machines, but greater than previously reported in independence experiments.