# Updating the silent speech challenge benchmark with deep learning

arXiv: https://arxiv.org/abs/1709.06818 · HTML: https://mlsanity.com/paper/1709.06818/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

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

- [Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition](https://mlsanity.com/paper/2206.07327/) (2022)
- [LipLearner: Customizable Silent Speech Interactions on Mobile Devices](https://mlsanity.com/paper/2302.05907/) (2023)
- [SottoVoce: An Ultrasound Imaging-Based Silent Speech Interaction Using Deep Neural Networks](https://mlsanity.com/paper/2303.01758/) (2023)
- [Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism](https://mlsanity.com/paper/2106.11769/) (2021)
- [Silent versus modal multi-speaker speech recognition from ultrasound and video](https://mlsanity.com/paper/2103.00333/) (2021)
- [Automatic audiovisual synchronisation for ultrasound tongue imaging](https://mlsanity.com/paper/2105.15162/) (2021)
- [IR-UWB Radar-Based Contactless Silent Speech Recognition of Vowels, Consonants, Words, and Phrases](https://mlsanity.com/paper/2312.09572/) (2023)

## Abstract (arXiv)

The 2010 Silent Speech Challenge benchmark is updated with new results obtained in a Deep Learning strategy, using the same input features and decoding strategy as in the original article. A Word Error Rate of 6.4% is obtained, compared to the published value of 17.4%. Additional results comparing new auto-encoder-based features with the original features at reduced dimensionality, as well as decoding scenarios on two different language models, are also presented. The Silent Speech Challenge archive has been updated to contain both the original and the new auto-encoder features, in addition to the original raw data.