Feature extraction based on bio-inspired model for robust emotion recognition

Emotional state identification is an important issue to achieve more natural speech interactive systems. Ideally, these systems should also be able to work in real environments in which generally exist some kind of noise. Several bio-inspired representations have been applied to artificial systems for speech processing under noise conditions. In this work, an auditory signal representation is used to obtain a novel bio-inspired set of features for emotional speech signals. These characteristics, together with other spectral and prosodic features, are used for emotion recognition under noise conditions. Neural models were trained as classifiers and results were compared to the well-known mel-frequency cepstral coefficients. Results show that using the proposed representations, it is possible to significantly improve the robustness of an emotion recognition system. The results were also validated in a speaker independent scheme and with two emotional speech corpora.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Technology clubs, technology gaps and growth trajectories

This paper looks at the convergence clubs literature from a Schumpeterian perspective, and it follows the idea that cross-country differences in the ability to innovate and to imitate foreign technologies determine the existence of clustering, polarization and convergence clubs. The study investigates the characteristics of different technology clubs and the growth trajectories that they have followed over time. The cross-country empirical analysis first explores the existence of multiple regimes in the data by means of cluster analysis techniques. It then estimates a technology-gap growth equation in a dynamic panel model specification. The empirical results identify three distinct technology clubs, and show that these are characterized by remarkably different technological characteristics and growth behavior.Growth and development; technological change; convergence clubs; polarization

Pacific Islands' Bilateral Trade: The Role of Remoteness and of Transport Costs

Bilateral trade of geographically distant countries is likely to be negatively affected by the distance separating them from their trading partners and positively affected by their remoteness, defined as the average weighted distance between two countries with weights reflecting the absorptive capacity of the partner country. In presence of competitive transport costs, the effect of remoteness and distance is diluted. An augmented gravity model applied to the Pacific islands' bilateral trade from 1980 to 2004 shows that a doubling of the elasticity of distance would decrease their average bilateral trade by 80 per cent. Remoteness positively affects the Pacific islands' bilateral trade, but does not compensate for the negative effect of distance. The opposite is found for the Caribbean islands, where the elasticity of trade with respect to remoteness is eight times bigger than that for the Pacific islands. ...bilateral trade, remoteness, transport costs, infrastructure, gravity model, Pacific islands

A Neural Attention Model for Categorizing Patient Safety Events

Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.Comment: ECIR 201