A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol. The database includes eight unimodal biometric traits, namely: speech, iris, face (still images, videos of talking faces), handwritten signature and handwritten text (on-line dynamic signals, off-line scanned images), fingerprints (acquired with two different sensors), hand (palmprint, contour-geometry) and keystroking. The database comprises 400 subjects and presents features such as: realistic acquisition scenario, balanced gender and population distributions, availability of information about particular demographic groups (age, gender, handedness), acquisition of replay attacks for speech and keystroking, skilled forgeries for signatures, and compatibility with other existing databases. All these characteristics make it very useful in research and development of unimodal and multimodal biometric systems.

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Over the last few years, we have witnessed a growing interest in computer-assisted pronunciation training (CAPT) tools and the commercial success of foreign language teaching applications that incorporate speech synthesis and automatic speech recognition technologies. However, empirical evidence supporting the pedagogical effectiveness of these systems remains scarce. In this study, a minimal-pair based CAPT tool that implements exposure-perception-production cycles and provides automatic feedback to learners is tested for effectiveness in training adult native Spanish users (English level B1-B2) in the production of a set of difficult English sounds. Working under controlled conditions, a group of users took a pronunciation test before and after using the tool. Test results were considered against those of an in-classroom group who followed similar training within the traditional classroom setting. Results show a significant pronunciation improvement among the learners who used the CAPT tool, as well as a correlation between human rater's assessment of post-tests and automatic CAPT assessment of users.

Availability and usability of mobile smart devices and speech technologies ease the development of language learning applications, although many of them do not include pronunciation practice and improvement. A key to success is to choose the correct methodology and provide a sound experimental validation assessment of their pedagogical effectiveness. In this work we present an empirical evaluation of Japañol, an application designed to improve pronunciation of Spanish as a foreign language targeted to Japanese people. A structured sequence of lessons and a quality assessment of pronunciations before and after completion of the activities provide experimental data about learning dynamics and level of improvement. Explanations have been included as corrective feedback, comprising textual and audiovisual material to explain and illustrate the correct articulation of the sounds. Pre-test and post-test utterances were evaluated and scored by native experts and automatic speech recognition, showing a correlation over 0.86 between both predictions. Sounds [s], [fl], [R] and [s], [fR], [T] explain the most frequent perception and production failures, respectively, which can be exploited to plan future versions of the tool, including gamified ones. Final automatic scores provided by the application highly correlate (r>0.91) to expert evaluation and a significant pronunciation improvement can be measured.

Prosody is a fundamental speech element responsible for communicative functions such as intonation, accent and phrasing, and prosodic impairments of individuals with intellectual disabilities reduce their communication skills. Yet, technological resources have paid little attention to prosody. This study aims to develop an automatic classifier to predict the prosodic quality of utterances produced by individuals with Down syndrome, and to analyse how inter-individual heterogeneity affects assessment results. A therapist and an expert in prosody judged the prosodic appropriateness of a corpus of Down syndrome' utterances collected through a video game. The judgments of the expert were used to train an automatic classifier that predicts prosodic quality by using a set of fundamental frequency, duration and intensity features. The classifier accuracy was 79.3% and its true positive rate 89.9%. We analyzed how informative each of the features was for the assessment and studied relationships between participants' developmental level and results: interspeaker variability conditioned the relative weight of prosodic features for automatic classification and participants' developmental level was related to the prosodic quality of their productions. Therefore, since speaker variability is an intrinsic feature of individuals with Down syndrome, it should be considered to attain an effective automatic prosodic assessment system.

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Learning games have a remarkable potential for education. They provide an emergent form of social participation that deserves the assessment of their usefulness and efficiency in learning processes. This study describes a novel learning game for foreign pronunciation training in which players can challenge each other. Native Spanish speakers performed several pronunciation activities during a one-month competition using a mobile application, designed under a minimal pairs approach, to improve their pronunciation of English as a foreign language. This game took place in a competitive scenario in which students had to challenge other participants in order to get high scores and climb up a leaderboard. Results show intense practice supported by a significant number of activities and playing regularity, so the most active and motivated players in the competition achieved significant pronunciation improvement results. The integration of automatic speech recognition (ASR) and text-to-speech (TTS) technology allowed users to improve their pronunciation while being immersed in a highly motivational game. INDEX TERMS Computer-assisted pronunciation training, mobile learning game, mobile application, English L2 pronunciation, challenges, motivation.

This article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training . Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.