# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
###### Abstract
This phenomenon has come to be generalized nowadays with the advent of deep learning methods (DL), which rely on intensive computation, proposing pure EE models based on DD systems, in contrast to MD systems, where substantial efforts must be devoted to extracting and selecting semantic features \"by hand\", on what has become to be known as handcrafted-feature systems (HF). This panorama would be an idealized one, and when facing real clinical scenarios the struggle would be won by EE systems if it were not for the data complexity and variability, as very often, EE systems are unreliable when it comes to generalization. Concept EE-based solutions, which work rather efficiently under very controlled conditions, are prone to fail dramatically when exposed to slightly varying situations differing from the original ones under which they were conceived and trained [8]. Careful feature processing is another problem to deal with. Complex AI system design requires recording multi-trait signals from different perspectives, including high-dimensional feature spaces, which can hinder pattern descriptions if datasets are incomplete, sparse, or corrupted. Ideally, the problem should concentrate on sets of features that maximally separate data classes, still retaining classification-related semantic meaning. If estimated clinical outcomes are expected to be continuous, regression methods will be used. As far as voice is concerned, this problem becomes more and more complicated, because the biomarkers that can be estimated from the acoustic signal are of a broad nature [9], such as those found in phonation (including respiration, common perturbation, harmonic-noise ratios, open and close quotients, biomechanical parameters, etc. ), articulation (spectrograms, cepstrograms, formant-grams, vowel space area, formant centralization ratios, kinematic distributions, etc. ), prosody (energy and pitch contours, zero crossings, etc. ), and rhythmic (syllable/silence counts and ratios, speech rate, etc.). The statistical description of each of these sets of observations becomes the playground on which feature estimation is based, followed by identifying robust feature subsets and selecting the most relevant ones. Traditionally in MD-based ML, the semantic load of each class is taken into consideration in feature selection (understanding by semantic load, the capability of associating each feature with a specific observed phonation behavior of clinical interest). However, in some cases where features are exclusively selected on purely statistical terms, they might lose part of their semantic load, especially when applying strongly compressing feature transformations. In general, when developing automatic ML approaches concerning Voice Quality Assessment, it is of crucial interest to take into account the following considerations:  
* Voice pathology does not involve a single concept, but many. According to the American Speech and Hearing Association [10], voice disorders may be classified into two main categories: organic (produced by alteration in the respiratory, laryngeal, or oro-naso-pharyngeal mechanisms) or functional (produced by the inefficient use of phonation mechanisms). To help conceptually visualize the difficulties involved, two examples are shown in Figures 1 and 2, corresponding to the phonation of a sustained vowel [a:] during 4 s, the first one from a normative participant, and the second one from a patient suffering from larynx carcinoma. These two examples illustrate the complexity of defining acoustic correlates with a semantic load informing about the nature and severity of a given phonation alteration produced by a specific pathological laryngeal disease.