# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 3 Review of Selected Works
### Review Results  
In this section, a critical review of different ML publications on laryngeal pathology assessment is presented. Intensive and comprehensive reviews of different approaches to pathological voice assessment may be found in [65, 66, 67, 68]. Interesting recent original research on multimodal and poly-syndrome MD approaches can be found in [17, 18, 28, 69, 70, 71, 72, 73, 74, 75, 76]. Recent approaches in the domain of DD pathological voice assessment are worth being mentioned [6, 31, 77]. Table 2 gives the list of the selected publications and their qualification concerning the characteristics mentioned (marked as \"X\" when fulfilling EPAS, SVP, or CR, \"MD\", or \"DD\" for DD/MD, and \"O\" or \"R\" for O/R).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **EPAS** & **SVP** & **DD/MD** & **CR** & **O/R** \\ \hline [PERSON] et al. [61] & X & X & DD (c) & X & O \\ [PERSON] et al. [27] & & X & MD and DD (b,c) & X & O \\ [PERSON] and [PERSON] [70] & X & X & MD (b,c) & X & O \\ [PERSON] et al. [17] & X & X & MD (a, b) & X & O \\ [PERSON] et al. [69] & X & X & MD (a, b) & X & O \\ [PERSON] et al. [28] & X & X & MD (b) & X & O \\ [PERSON], [PERSON], and [PERSON] [74] & & & MD (b) & X & O \\ [PERSON], [PERSON], and [PERSON] [71] & X & X & MD (a,b) & & O \\ [PERSON] et al. [20] & & X & DD (a,c) & & O \\ [PERSON] et al. [18] & X & X & MD (a,b) & & O \\ [PERSON], [PERSON], and [PERSON] [72] & X & X & MD (b) & X & O \\ [PERSON] et al. [66] & & & MD (a, b) & & R \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pathological speech and voice evaluation approaches reviewed in the present study (Selected accordingly to the criteria before mentioned: addressing laryngeal pathology characterization from acoustical signal analysis, having been published since 2010, disclosing classification or regression methodology, based on standard databases or supplementary materials to allow reproducibility, supported by reliable performance metrics, and contributing novel and original work.). EPAS: Exploratory or prospective advanced statistics; SVP: Statistical validation of performance; DD/MD: Data-driven or model-driven (the label within parenthesis indicates the strategy of the approach regarding Figure 3, as a, b, c, d, or a combination); CR: Clinical relevance; O/R: Original contribution or review.  
Figure 3: Different strategies for ML pathology detection and classification: (**a**) Handcrafted feature framing, comprising feature estimation followed by feature selection, based on physiological and acoustical models of voice production to estimate input features; (**b**) Model-driven approach, using features extracted by (**a**) and detection and classification model rules and mathematical structures.  
such as Gaussian mixture models, random forests, shallow neural networks, support vector machines, etc. to estimate classification and performance scores; (**c**) Data-driven approach, also using features modeled by (**a**) and detection and classification by deep learning methods as deep neural networks, associating expected output targets on intensive numerical computation to estimate classification and performance scores; (**d**) End-to-end approach, where stage (**a**) is omitted, using direct raw input data and desired output targets to produce classification and performance scores. Classically, MD (**b**) and DD (**c**) approaches take input features provided by a previous HF stage (**a**). End-to-end approaches do not require a previous HF stage (**a**).