  Acoustic-to-articulatory speech inversion could enhance automated clinical
mispronunciation detection to provide detailed articulatory feedback
unattainable by formant-based mispronunciation detection algorithms; however,
it is unclear the extent to which a speech inversion system trained on adult
speech performs in the context of (1) child and (2) clinical speech. In the
absence of an articulatory dataset in children with rhotic speech sound
disorders, we show that classifiers trained on tract variables from
acoustic-to-articulatory speech inversion meet or exceed the performance of
state-of-the-art features when predicting clinician judgment of rhoticity.
Index Terms: rhotic, speech sound disorder, mispronunciation detection
