# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
###### Abstract  
Pathological voice characterization has received increasing attention over the last 20 years. Hundreds of studies have been published showing inventive approaches with very promising findings. Nevertheless, methodological issues might hamper performance assessment trustworthiness. This study reviews some critical aspects regarding data collection and processing, machine learning-oriented methods, and grounding analytical approaches, with a view to embedding developed clinical decision support tools into the diagnosis decision-making process. A set of 26 relevant studies published since 2010 was selected through critical selection criteria and evaluated. The model-driven (MD) or data-driven (DD) character of the selected approaches is deeply examined considering novelty, originality, statistical robustness, trustworthiness, and clinical relevance. It has been found that before 2020 most of the works examined were more aligned with MD approaches, whereas over the last two years a balanced proportion of DD and MD-based studies was found. A total of 15 studies presented MD characters, whereas seven were mainly DD-oriented, and four shared both profiles. Fifteen studies showed exploratory or prospective advanced statistical analysis. Eighteen included some statistical validation to avail claims. Twenty-two reported original work, whereas the remaining four were systematic reviews of others' work. Clinical relevance and acceptability by voice specialists were found in 14 out of the 26 works commented on. Methodological issues such as detection and classification performance, training and generalization capability, explainability, preservation of semantic load, clinical acceptance, robustness, and development expenses have been identified as major issues in applying machine learning to clinical support systems. Other important aspects to be taken into consideration are trustworthiness, gender-balance issues, and statistical relevance. 1  
Article  
The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal  
1  
[PERSON], A.  
of the biophysical hypotheses behind them. MD systems work under the assumption that a model that encapsulates the behavior of a system can be developed [2; 3], and by changing the initial conditions and the model parameters the outcome can be predicted. DD systems work the other way around--the working model is not something to be used as a vehicle to understand the data, on the belief that data speak for themselves [4]. The complex mathematical model could be left aside as far as the behavior of the system might be inferred from the distribution of the data. Traditionally, both approaches have been assumed as opposite and competing, whereas exploring their synergies may procure new opportunities [5]. A recent example of these different approaches in pathological phonation detection can be found in [6], where results from glottal features and a support vector machine (SVM) classifier are compared with those from raw speech and glottal flow on a convolutional neural network (CNN) and a long-short term memory (LSTM) classifier, on what is known as an end-to-end (EE) approach. Implicitly, the use of glottal features assumes the need of resorting to the classical source-filter model proposed by [PERSON] [7], thus the approach can be considered partially as an MD one. The use of raw speech on a CNN or an LSTM would be a pure DD approach. A system based on processing a raw glottal source on a CNN or an LSTM, also proposed in the same article, might be considered a hybrid approach, much in the way as glottal source estimation by inversion methods does, where a source-filter model is implicit. Eventually, DD systems may work more efficiently than knowledge-based systems, as they do not depend on accurate and robust systemic descriptions, and are prone to be incomplete, vulnerable, and of cumbersome implementation, whereas DD models depend on robust and compact numerical algorithms.