Replication-based regularization approaches to diagnose Reinke's edema by using voice recordings

Abstract

Reinke's edema is one of the most prevalent laryngeal pathologies. Its detection can be addressed by using computer-aided diagnosis systems based on features extracted from speech recordings. When extracting acoustic features from different voice recordings of a particular subject at a concrete moment, imperfections in technology and the very biological variability result in values that are close, but they are not identical. This suggests that the within-subject variability must be properly addressed in the statistical methodology. Regularization-based regression approaches can be used to reduce the classification errors by favoring the best predictors and penalizing the worst ones. Three replication-based regularization approaches for variable selection and classification have been specifically designed and implemented to take into account the underlying within-subject variability. In order to illustrate the applicability of these approaches, an experiment has been specifically conducted to discriminate Reinke's edema patients (30 subjects) from healthy people (30 subjects) in a hospital environment. The features have been extracted from four phonations of the sustained vowel /a/ recorded for each subject, leading to a database that has fed the proposed machine learning approaches. The proposed replication-based approaches have been proved to be reliable in terms of selected features and predictive ability, leading to a stable accuracy rate of 0.89 under a cross-validation framework. Also, a comparison with traditional independence-based regularization methods reports a great variability of the latter in terms of selected features and accuracy metrics. Therefore, the proposed approaches contribute to fill a gap in the scientific literature on statistical approaches considering within-subject variability and can be used to build a robust expert system.