Speech Analysis of Patients with Cleft Palate Using Artificial Intelligence Techniques: A Systematic Review

Introduction: This systematic review examines the use of machine learning (ML) algorithms to detect hypernasal speech in patients with cleft palate (CP), which can persist after primary repair surgery, and require revision. Due to a shortage of speech language pathologists (SLPs), automated detection tools could help improve access to care in underserved areas. The study compares the characteristics and accuracy of different types of machine learning algorithms. Methods: On August 18, 2021, searches were conducted across 8 databases: PubMed, SCOPUS, Cochrane, IEEE, ACM, L&LB, PsychInfo, and CINAHL. Search terms used were: (Artificial Intelligence OR Machine Learning OR Neural networks AND Cleft lip OR Cleft palate OR Hypernasality OR Velopharyngeal Insufficiency). To be included, papers needed to describe ML algorithms for CP speech detection and report concordance to human professional speech clinicians. Results: Database searches yielded 135 unique articles. Five articles met full inclusion criteria and 3 additional articles were identified by hand searching references of articles that passed initial screening. These algorithms were categorized as either Feature Dependent non-Deep learning (n = 5) or Feature Dependent deep learning (n = 2) algorithms or Feature Independent deep learning (n = 3) algorithms. Their pooled average concordance were 0.85, 0.93, and 0.91 respectively. Their average training database sizes were 3587, 3921, and 6306 speech samples respectively. Conclusion: Machine learning algorithms have been shown to be an effective tool for the evaluation of hypernasal speech. This systematic review has shown that ML algorithms are able to detect hypernasality with high concordance, consistent with professional speech language clinicians in a rapid, and autonomous manner. ML algorithms can extend the reach of speech language pathologists and complement their gold standard, this long-term outcome monitoring has great potential to improve treatment outcomes.