  Speech sound disorders are a common communication impairment in childhood.
Because speech disorders can negatively affect the lives and the development of
children, clinical intervention is often recommended. To help with diagnosis
and treatment, clinicians use instrumented methods such as spectrograms or
ultrasound tongue imaging to analyse speech articulations. Analysis with these
methods can be laborious for clinicians, therefore there is growing interest in
its automation. In this paper, we investigate the contribution of ultrasound
tongue imaging for the automatic detection of speech articulation errors. Our
systems are trained on typically developing child speech and augmented with a
database of adult speech using audio and ultrasound. Evaluation on typically
developing speech indicates that pre-training on adult speech and jointly using
ultrasound and audio gives the best results with an accuracy of 86.9%. To
evaluate on disordered speech, we collect pronunciation scores from experienced
speech and language therapists, focusing on cases of velar fronting and gliding
of /r/. The scores show good inter-annotator agreement for velar fronting, but
not for gliding errors. For automatic velar fronting error detection, the best
results are obtained when jointly using ultrasound and audio. The best system
correctly detects 86.6% of the errors identified by experienced clinicians. Out
of all the segments identified as errors by the best system, 73.2% match errors
identified by clinicians. Results on automatic gliding detection are harder to
interpret due to poor inter-annotator agreement, but appear promising. Overall
findings suggest that automatic detection of speech articulation errors has
potential to be integrated into ultrasound intervention software for
automatically quantifying progress during speech therapy.
