The extent of research on children’s speech in general and on disordered speech specifically is very limited. In this article, we describe the process of creating databases of children’s speech and the possibilities for using such databases, which have been created by the LANNA research group in the Faculty of Electrical Engineering at Czech Technical University in Prague. These databases have been principally compiled for medical research but also for use in other areas, such as linguistics. Two databases were recorded: one for healthy children’s speech (recorded in kindergarten and in the first level of elementary school) and the other for pathological speech of children with a Specific Language Impairment (recorded at a surgery of speech and language therapists and at the hospital). Both databases were sub-divided according to specific demands of medical research. Their utilization can be exoteric, specifically for linguistic research and pedagogical use as well as for studies of speech-signal processing.

Aim To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow. Background Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient. Methods A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) system database of the Gemini Eye Clinic. The dataset was optimized and split into the selection set (used in the design for models and training), and the verification set (used in the evaluation). The set of mean prediction errors (PEs) and the distribution of predicted refractive errors were evaluated for both models and clinical results (CR). Results Both models performed significantly better for the majority of the evaluated parameters compared with the CR. There was no significant difference between both evaluated models. In the ±0.50 D PE category both SVM-RM and MLNN-EM were slightly better than the Barrett Universal II formula, which is often presented as the most accurate calculation formula. Conclusion In comparison to the current clinical method, both SVM-RM and MLNN-EM have achieved significantly better results in IOL calculations and therefore have a strong potential to improve clinical cataract refractive outcomes.

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The ability to use the spoken language is one of the most important characteristics in child development. Speech is difficult to replace in real life, although there are several other options for communication. Inabilities to communicate with speech skills can isolate children from society, especially children with specific language impairments. This research study focused on a specific disorder, known as specific language impairment (SLI); in the Czech language, it is specifically known as developmental dysphasia (DD). One major problem is that this disorder is detected at a relatively late age. Early diagnosis is critical for successful speech therapy in children. The current chapter presents several different approaches to solve this issue, including a simple test for detecting this disorder. One approach involves the use of an original iPad application for detecting SLI based on the number of pronunciation errors in utterances. One advantage of this method is its simplicity; anyone can use it, including parents.

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