Document Type
Preprint
Publication Date
1-1-2023
Journal / Book Title
Proceedings of the Annual Conference of the International Speech Communication Association Interspeech
Abstract
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.
DOI
10.21437/Interspeech.2023-1924
Montclair State University Digital Commons Citation
Benway, Nina R.; Siriwardena, Yashish M.; Preston, Jonathan L.; Hitchcock, Elaine; McAllister, Tara; and Espy-Wilson, Carol, "Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of/ɹ/in Child Speech Sound Disorders" (2023). Department of Communication Sciences and Disorders Faculty Scholarship and Creative Works. 185.
https://digitalcommons.montclair.edu/communcsci-disorders-facpubs/185