Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of/ɹ/in Child Speech Sound Disorders

Nina R. Benway, Yashish M. Siriwardena, Jonathan L. Preston, Elaine Hitchcock, Tara McAllister, Carol Espy-Wilson

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)4568-4572
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2023-August
DOIs
StatePublished - 2023
Event24th International Speech Communication Association, Interspeech 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023

Keywords

  • mispronunciation detection
  • rhotic
  • speech sound disorder

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