Complementary Approaches for Improving Nasality Assessment in Cleft Palate Patients: Exploring Nasal Signal-Based Acoustic Feature and Visual Feature Analysis in Stereo Signals

Abstract

Background. Cleft Palate (CP) is a common congenital condition affecting approximately 500 infants born with orofacial clefts yearly in Canada (Public Health Infobase, 2017). It disrupts the velopharyngeal sphincter, leading to oral-nasal balance disorders such as hypernasality and hyponasality. Accurate detection and classification of oral–nasal balance disorders are essential for optimizing treatment outcomes. Although auditory–perceptual judgments remain the primary method for evaluating nasality, their inherent subjectivity limits reliability. Integrating complementary measures with auditory assessments can enhance diagnostic accuracy and support better treatment planning and follow-up. Objective. This study aimed to improve the accuracy of auditory-perceptual assessments by incorporating acoustic and visual analyses as complementary tools. The research consisted of three phases. The first study focused on exploring effective acoustic parameters extracted solely from nasal signals to develop a multiparametric approach for automatically classifying simulated oral-nasal balance disorders using linear discriminant analysis (LDA). The second study applied the developed multiparametric acoustic diagnostic algorithm to a retrospective dataset of children with cleft palate to evaluate its clinical applicability. The third study conducted a pilot investigation into the use of visual pattern inspection in stereo signals for classifying oral-nasal balance disorders. Methods. All three studies used a retrospective, observational design based on secondary analysis of pre-existing speech data. The first and third studies employed simulated voice samples, while the second study used both simulated and clinical samples from children with cleft palate. Results. In Study 1, LDA showed 90.9% accuracy in classifying simulated conditions based on nasal channel acoustic energy. This model demonstrated potential as a cost-effective and quantitative alternative to tools like nasometry. Study 2 confirmed that nasal-signal-derived acoustic parameters could differentiate between normal, hypernasal, and hyponasal conditions but highlighted challenges in generalizing models across datasets. Study 3 achieved an overall classification accuracy of 85.15% using visual analysis of paired oscillogram images. The highest accuracy (100%) was observed for hyponasality classification, while the lowest accuracy (61%) was recorded for mixed nasality classification. Conclusion. This thesis advances the field of nasality assessment by demonstrating the potential of acoustic analysis of the nasal signal in isolation, as well as a new visual method for detecting and classifying oral-nasal balance disorders. These approaches could be integrated with traditional auditory-perceptual evaluations to enhance diagnostic accuracy, guide treatment planning, and support follow-up care. This research contributes new practical diagnostic approaches that could become directly relevant to clinical populations such as individuals with cleft palate or neurological disorders. The present findings lay a foundation for future work to translate these research innovations into clinical practice.