# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
###### Abstract
Organic disorders of laryngeal origin may be sub-classified into structural (physical changes or damage to mechanisms involved in phonation, such as the vocal folds, or the larynx supporting structure, muscles, and cartilages) or neurogenic (resulting from malfunction of the central or peripheral nervous system pathways to larynx, resulting in tremor, spasmodic dysphonia, or vocal fold paralysis--this might also be of unilateral or bilateral character). Functional disorders might be the result of vocal fatigue, muscle dystonia (aphasia, dysphonia, hypertension), irregular vibration modality (diplophonia), or improper phonation (ventricular). Besides, according to biomechanical behavior observation, they can be classified as induced by deficient glottal cyclicity or by asymmetrical vibration. Vocal fold-related disorders may be classified accordingly to their influence on vocal fold vibration as unilateral (polyps, cysts, edema, granulomatosis, sulci, etc.) or bilateral (noduli, papillomavirus, bilateral palsy, etc.). Systemic larynx-related disorders may include chronic laryngitis, carcinomas, etc. The critical question is that this polyhedral panorama might sometimes be ignored by publications in machine-learning laryngeal pathology characterization, although they strongly condition the behavior of the biomechanical system and the acoustical signals, which is the ground on which pathology detection and classification stand. It is really difficult to classify and even talk about a multi-perspective problem when it is not visualized, not to mention interpretability and explainability, bearing in mind that many of these disorders induce similar observable correlates (many-to-many regression and classification problems [11]). * Many studies tend to use a small set of publicly available databases, which include a few sustained vowels only. In other words, they might not be sufficiently representative of the pathologies targeted and of the population affected, overtraining on a limited dataset, hampering generalization. In this sense, the following publicly available databases have become de facto benchmarks, among others: Saarbrucken Voice Database (SVD) [12], PC-GITA [13], Hospital Universitario Principe de Asturias Database (PdADb) [14], and Massachusetts Eye and Ear Infirmary (MEEI) [15]. * When using third-source databases, researchers should assess the production standards under which they had been produced to ensure that they are carefully produced regarding the use of consistent equipment, adequate acoustic settings, sound pressure levels (SPL), and fundamental frequency calibration. * Numerous studies claim diagnostic capabilities, opening a controversial debate, as most clinicians in laryngeal pathology assessment would be skeptical of binary classifiers claiming diagnosis (for instance to decide if a patient is affected by organic or functional disorders) based on acoustical correlates only. Clinical assessment practice relies on visual and image inspection tools (for instance video laryngoscopy). What clinicians consider useful instead is a methodology to assess voice functionality during and after pharmacological or rehabilitative treatment to evaluate recovery or functional improvement. Besides, ML systems are expected to also provide information regarding what role a feature plays in treatment, and its efficiency on detection and classification performance, as well as the risk of inducing confounding factors. The term explainable medicine (EM) is the key concept in this sense [16]. * Quite a few studies on laryngeal pathology characterization by ML methods offer some knowledge of voice production and vocal pathologies when including thoughts as \"data speaks for itself\", implying that such a knowledge background does not matter at all. This could be a major cause for ML rejection or unacceptability by some clinical experts and would not be doing any good to the advancement of ML applicability in this field.