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In the context of the detection and evolutionary control of Alzheimer’s disease from voice recordings and their automatic processing, this work aims to objectively determine the discriminatory capacity of a set of voice features linked to the emotional load of speech. We use descriptive statistics derived from the concept of emotional temperature as quantifiable characteristics of the voice. We apply a series of parametric and nonparametric analyses to the set of features, both individually and collectively, and explore their potential in relation to the use of different methods of unsupervised classification. With the aim of comparing how the type of interviewer used in the sample collection (i.e., voice recordings) influences the discrimination of AD through emotional speech analysis, we used the CSAP-19 database, which includes voice samples obtained through human interviewer (spontaneous speech samples) and automatic interviewer (induced speech samples) for the three defined populations (HC, mild AD, and moderate AD). In this regard, a comparative analysis is also conducted on the potential of emotional temperature features defined according to the sample collection process (manual or automatic interview process).

Currently, technological development is making its way into education through the increasing integration of new technologies into teaching. In this context, gamification emerges as one of the ways to incorporate the pedagogical process into the digital world, creating the need to encourage both the digital skills of educators and students. The objective of this work is to shed light on the growing use of technology-based teaching methods and encourage their integration into educational practice from the teacher’s point of view. This work specifically analyses the European Framework for Digital Competence of Educators, relates the different digital competences defined to a series of localized educational solutions, and proposes a direct relationship between different gamification tools and serious games. Based on the review carried out, this study highlights a profound change in the educational process that involves digitization and the use of new technologies to optimize learning, as well as the training needs of teaching staff and the development of educational solutions that take into account the different digital competences.

Currently, there are more and more frequent studies focused on the evaluation of Alzheimer’s disease (AD) from the automatic analysis of the speech of patients, in order to detect the presence of the disease in an individual or for the evolutionary control of the disease. However, studies focused on analyzing the effect of the methodology used to generate the spontaneous speech of the speaker who undergoes this type of analysis are rare. The objective of this work is to study two different strategies to facilitate the generation of the spontaneous speech of a speaker for further analysis: the use of a human interviewer that promotes the generation of speech through an interview and the use of an automatic system (an automatic interviewer) that invites the speaker to describe certain visual stimuli. In this study, a database called Cross-Sectional Alzheimer Prognosis R2019 has been created, consisting of speech samples from speakers recorded using both methodologies. The speech recordings have been studied through a feature extraction based on five basic temporal measurements. This study demonstrates the discriminatory capacity between the speakers with AD and the control subjects independent of the strategy used in the generation of spontaneous speech. These results are promising and can serve as a basis for knowing the effectiveness and extension of automated interview processes, especially in telemedicine and telecare scenarios.