# Automatic audiovisual synchronisation for ultrasound tongue imaging

arXiv: https://arxiv.org/abs/2105.15162 · HTML: https://mlsanity.com/paper/2105.15162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization](https://mlsanity.com/paper/1807.00230/) (2018)
- [Perfect match: Improved cross-modal embeddings for audio-visual synchronisation](https://mlsanity.com/paper/1809.08001/) (2018)
- [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)
- [DNN-based Acoustic-to-Articulatory Inversion using Ultrasound Tongue Imaging](https://mlsanity.com/paper/1904.06083/) (2019)
- [Synchronising audio and ultrasound by learning cross-modal embeddings](https://mlsanity.com/paper/1907.00758/) (2019)
- [Exploiting ultrasound tongue imaging for the automatic detection of speech articulation errors](https://mlsanity.com/paper/2103.00324/) (2021)

## Abstract (arXiv)

Ultrasound tongue imaging is used to visualise the intra-oral articulators during speech production. It is utilised in a range of applications, including speech and language therapy and phonetics research. Ultrasound and speech audio are recorded simultaneously, and in order to correctly use this data, the two modalities should be correctly synchronised. Synchronisation is achieved using specialised hardware at recording time, but this approach can fail in practice resulting in data of limited usability. In this paper, we address the problem of automatically synchronising ultrasound and audio after data collection. We first investigate the tolerance of expert ultrasound users to synchronisation errors in order to find the thresholds for error detection. We use these thresholds to define accuracy scoring boundaries for evaluating our system. We then describe our approach for automatic synchronisation, which is driven by a self-supervised neural network, exploiting the correlation between the two signals to synchronise them. We train our model on data from multiple domains with different speaker characteristics, different equipment, and different recording environments, and achieve an accuracy >92.4% on held-out in-domain data. Finally, we introduce a novel resource, the Cleft dataset, which we gathered with a new clinical subgroup and for which hardware synchronisation proved unreliable. We apply our model to this out-of-domain data, and evaluate its performance subjectively with expert users. Results show that users prefer our model's output over the original hardware output 79.3% of the time. Our results demonstrate the strength of our approach and its ability to generalise to data from new domains.