Multicondition Training for Noise-Robust Detection of Benign Vocal Fold Lesions From Recorded Speech

This study evaluates the effects of Multicondition Training (MCT) on computer aided diagnosis systems for voice quality assessment associated to exudative lesions of Reinke's space. This technique adds various noise conditions to the speech recordings in order to recreate realistic acoustic environments. Four different databases (Massachussets Eye and Ear Infirmary, UEX-Voice, Saarbrücken, and Hospital Universitario Príncipe de Asturias) recorded in very different acoustic environments are used. We compare the outcomes of random forest classifier models comprising feature selection, hyperparameter tuning, and cross-validation attending the specific MCT schema used to separate healthy from pathological subjects for three diseases (nodules, polyps, and Reinke's edema). Apart from the clean case baseline, an asymmetric (one subject recording is affected only by one noise recording) and two symmetric (one subject recording is affected by all the noise recordings) noise-based MCT scenarios are considered. These scenarios are created by adding realistic acoustic noise of different types to the sustained /a/ vowel recordings. The symmetric approaches are affected by methodological concerns and are tested with a comparative purpose, to emphasize these issues. Experimental results highlight the drawbacks of symmetric MCTs and exclude these techniques as a viable option. In contrast, asymmetric MCT is proven to be a suitable noise-robust approach to build a diagnosis system for exudative lesions of Reinke's space, as performance obtained with the resulting classifiers is not far from the performance obtained for clean training.