# Speaker-Adapted End-to-End Visual Speech Recognition for Continuous Spanish

arXiv: https://arxiv.org/abs/2311.12480 · HTML: https://mlsanity.com/paper/2311.12480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Deep Audio-Visual Speech Recognition](https://mlsanity.com/paper/1809.02108/) (2018)
- [LRS3-TED: a large-scale dataset for visual speech recognition](https://mlsanity.com/paper/1809.00496/) (2018)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [Visual Speech Recognition for Multiple Languages in the Wild](https://mlsanity.com/paper/2202.13084/) (2022)
- [ASR is all you need: cross-modal distillation for lip reading](https://mlsanity.com/paper/1911.12747/) (2019)
- [Audio-Visual Speech Recognition With A Hybrid CTC/Attention Architecture](https://mlsanity.com/paper/1810.00108/) (2018)
- [Sub-word Level Lip Reading With Visual Attention](https://mlsanity.com/paper/2110.07603/) (2021)
- [Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition](https://mlsanity.com/paper/2003.03206/) (2020)
- [Silent Speech Interfaces for Speech Restoration: A Review](https://mlsanity.com/paper/2009.02110/) (2020)
- [Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database](https://mlsanity.com/paper/1704.08028/) (2017)
- [TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition](https://mlsanity.com/paper/2008.00671/) (2020)
- [Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASR](https://mlsanity.com/paper/1907.04916/) (2019)
- [Speaker Adaptation for End-to-End CTC Models](https://mlsanity.com/paper/1901.01239/) (2019)

## Abstract (arXiv)

Different studies have shown the importance of visual cues throughout the speech perception process. In fact, the development of audiovisual approaches has led to advances in the field of speech technologies. However, although noticeable results have recently been achieved, visual speech recognition remains an open research problem. It is a task in which, by dispensing with the auditory sense, challenges such as visual ambiguities and the complexity of modeling silence must be faced. Nonetheless, some of these challenges can be alleviated when the problem is approached from a speaker-dependent perspective. Thus, this paper studies, using the Spanish LIP-RTVE database, how the estimation of specialized end-to-end systems for a specific person could affect the quality of speech recognition. First, different adaptation strategies based on the fine-tuning technique were proposed. Then, a pre-trained CTC/Attention architecture was used as a baseline throughout our experiments. Our findings showed that a two-step fine-tuning process, where the VSR system is first adapted to the task domain, provided significant improvements when the speaker adaptation was addressed. Furthermore, results comparable to the current state of the art were reached even when only a limited amount of data was available.