# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 2 ML Methods in Laryngeal Pathology Assessment
### Model-Driven vs. Data-Driven Assessment Systems
The types of features available for laryngeal pathology assessment are wide-ranging, basically of linear or non-linear type, and in the time, frequency, or time-frequency domains (as wavelet descriptions, for instance) [17]. The decision on which features are to be used by a given application is very much based on the previous experience and knowledge of the designer (that is where the term handcrafted features comes from), and is oriented to produce the best performance scores, depending on the loss function and the outcome for the application explored, for example, ROC curves for binary classification, confusion matrices for multi-class classification, and mean absolute error or mean squared error for regression applications. Statistical robustness and relevance are also important quality indices. Feature framing may be defined as feature estimation, followed up by feature selection to build a parsimonious information-rich dataset, using as few features as possible with maximal predictive ability. An important criterion to decide on feature selection is the semantic load of each feature considered within a joint statistical association of the selected feature subset toward maximizing the predictive power [38]. **Model-Driven Approach**: Model-driven approaches use formal model rules and low-middle computing-complexity algorithms based on a priori formal model rules or feature-target relationships, demanding a high parameter-tuning workload, assuming a priori knowledge about the process of feature generation. Generative methods such as Gaussian mixture models (GMMs), hidden Markov models (HMMs), or random forests (RFs) are typical classification methods used in this context. The tuning of these systems requires a certain amount of extra effort on hyper-parameter tweaking, and further back-annotation on feature estimation and classifier adjustment. **Data-Driven Approach**: The main difference concerning model-driven approaches relies on the fact that the associated algorithmic models treat the data generation mechanism as unknown, not making any assumptions on the stochastic model behind feature production [39]. Discriminative methods based on support vector machines (SVMs) or artificial neural networks (ANNs) are classical classification algorithmic methods used in this context. Specifically, the success of these methods depends on the level of complexity of the data processing architectures, which may be extremely high, as in the case of deep neural networks (DNNs), composed of many hidden layers using non-linear mapping functions which may create complex discrimination patterns in the projected hidden manifolds [40]. The foundations of DNNs are similar to ANNs, with an explosion of computational requirements, which makes them training-expensive, both in floating-point operations and in the amount of data for training, depending on robust input feature databases, associated with the desired outputs (targets or labels) in a supervised process. CNNs are a subset of DNNs that use bioinspired receptive fields to extract supposedly meaningful semantic atoms (elementary features) or micro-patterns of relevance in the hierarchical description of data structures. In essence, they are quite efficient systems if properly trained on adequate and well-populated databases. Nevertheless, these systems also require a certain amount of effort-demanding hyper-parameter tweaking. **End-to-End Approach**: These systems override the need for a preliminary feature estimation stage, as input raw data are fed directly to a very complex DNN with no other feature-extraction intermediate stage, as well as the desired outputs to be associated with. The main assumption (formally unproven, although experimentally accepted) is that data speak by themselves, in a full and complex associative process grabbed during the training phase embedded in the system topology and weights, in a pure DD behavior. Similarly to DD systems, the complexity of system adjustment moves from the feature space to hyperparameter space settings.