# Silent versus modal multi-speaker speech recognition from ultrasound and video

arXiv: https://arxiv.org/abs/2103.00333 · HTML: https://mlsanity.com/paper/2103.00333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [UltraSuite: A Repository of Ultrasound and Acoustic Data from Child Speech Therapy Sessions](https://mlsanity.com/paper/1907.00835/) (2019)
- [TaL: a synchronised multi-speaker corpus of ultrasound tongue imaging, audio, and lip videos](https://mlsanity.com/paper/2011.09804/) (2020)
- [Updating the silent speech challenge benchmark with deep learning](https://mlsanity.com/paper/1709.06818/) (2017)
- [Speaker-independent classification of phonetic segments from raw ultrasound in child speech](https://mlsanity.com/paper/1907.01413/) (2019)

## Abstract (arXiv)

We investigate multi-speaker speech recognition from ultrasound images of the tongue and video images of the lips. We train our systems on imaging data from modal speech, and evaluate on matched test sets of two speaking modes: silent and modal speech. We observe that silent speech recognition from imaging data underperforms compared to modal speech recognition, likely due to a speaking-mode mismatch between training and testing. We improve silent speech recognition performance using techniques that address the domain mismatch, such as fMLLR and unsupervised model adaptation. We also analyse the properties of silent and modal speech in terms of utterance duration and the size of the articulatory space. To estimate the articulatory space, we compute the convex hull of tongue splines, extracted from ultrasound tongue images. Overall, we observe that the duration of silent speech is longer than that of modal speech, and that silent speech covers a smaller articulatory space than modal speech. Although these two properties are statistically significant across speaking modes, they do not directly correlate with word error rates from speech recognition.