6 citations · 14 across the 6 of their papers we have counts for
6 papers
Perceptual Ratings Predict Speech Inversion Articulatory Kinematics in Childhood Speech Sound Disorders
Nina R. Benway, Saba Tabatabaee, Dongliang Wang +3
Purpose: This study evaluated whether articulatory kinematics, inferred by Articulatory Phonology speech inversion neural networks, aligned with perceptual ratings of /r/ and /s/ i…
A Tutorial on Clinical Speech AI Development: From Data Collection to Model Validation
Si-Ioi Ng, Lingfeng Xu, Ingo Siegert +4
There has been a surge of interest in leveraging speech as a marker of health for a wide spectrum of conditions. The underlying premise is that any neurological, mental, or physica…
CRoP: Context-wise Robust Static Human-Sensing Personalization
Sawinder Kaur, Avery Gump, Yi Xiao +5
The advancement in deep learning and internet-of-things have led to diverse human sensing applications. However, distinct patterns in human sensing, influenced by various factors o…
Prospective Validation of Motor-Based Intervention with Automated Mispronunciation Detection of Rhotics in Residual Speech Sound Disorders
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…
Classifying Rhoticity of /r/ in Speech Sound Disorder using Age-and-Sex Normalized Formants
Nina R Benway, Jonathan L Preston, Asif Salekin +3
Mispronunciation detection tools could increase treatment access for speech sound disorders impacting, e.g., /r/. We show age-and-sex normalized formant estimation outperforms ceps…
Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of /r/ in Child Speech Sound Disorders
Nina R Benway, Yashish M Siriwardena, Jonathan L Preston +3
Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispro…