Prospective Validation of Motor-Based Intervention with Automated Mispronunciation Detection of Rhotics in Residual Speech Sound Disorders

no code implementations • 30 May 2023 • Nina R Benway, Jonathan L Preston

Because lab accuracy of clinical speech technology systems may be overoptimistic, clinical validation is vital to demonstrate system reproducibility - in this case, the ability of the PERCEPT-R Classifier to predict clinician judgment of American English /r/ during ChainingAI motor-based speech sound disorder intervention.

Classifying Rhoticity of /r/ in Speech Sound Disorder using Age-and-Sex Normalized Formants

no code implementations • 25 May 2023 • Nina R Benway, Jonathan L Preston, Asif Salekin, Yi Xiao, Harshit Sharma, Tara McAllister

Mispronunciation detection tools could increase treatment access for speech sound disorders impacting, e. g., /r/.

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

no code implementations • 25 May 2023 • Nina R Benway, Yashish M Siriwardena, Jonathan L Preston, Elaine Hitchcock, Tara McAllister, Carol Espy-Wilson

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.