A Deep Learning Algorithm for Objective Assessment of Hypernasality in Children With Cleft Palate

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 10; pp. 2986 - 2996
• Main Authors: Mathad, Vikram C., Scherer, Nancy, Chapman, Kathy, Liss, Julie M., Berisha, Visar
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Algorithms; Artificial neural networks; Birth defects; Child; Children; Cleft lip/palate; Cleft palate; Cleft Palate - diagnosis; clinical speech analysis; Deep Learning; deep neural networks; Feature extraction; Humans; hypernasality; Machine learning; Neural networks; Protocols; Reliability; Reproducibility of Results; Speech; speech assessment; Speech Production Measurement; Surgery; Training; vocal biomarkers; Voice Disorders
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3058424

Objectives: Evaluation of hypernasality requires extensive perceptual training by clinicians and extending this training on a large scale internationally is untenable; this compounds the health disparities that already exist among children with cleft. In this work, we present the objective hypernasality measure (OHM), a speech-based algorithm that automatically measures hypernasality in speech, and validate it relative to a group of trained clinicians. Methods: We trained a deep neural network (DNN) on approximately 100 hours of a publicly-available healthy speech corpus to detect the presence of nasal acoustic cues generated through the production of nasal consonants and nasalized phonemes in speech. Importantly, this model does not require any clinical data for training. The posterior probabilities of the deep learning model were aggregated at the sentence and speaker-levels to compute the OHM. Results: The results showed that the OHM was significantly correlated with perceptual hypernasality ratings from the Americleft database ( r = 0.797, p < 0.001) and the New Mexico Cleft Palate Center (NMCPC) database ( r = 0.713, p < 0.001). In addition, we evaluated the relationship between the OHM and articulation errors; the sensitivity of the OHM in detecting the presence of very mild hypernasality; and established the internal reliability of the metric. Further, the performance of the OHM was compared with a DNN regression algorithm directly trained on the hypernasal speech samples. Significance: The results indicate that the OHM is able to measure the severity of hypernasality on par with Americleft-trained clinicians on thisdataset.