  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.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound;Image and Video Processing