Enrique Marcelo Albornoz

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The recognition of the emotional states of speaker is a multidisciplinary research area that has received great interest in the last years. One of the most important goals is to improve the voiced-based human-machine interactions. Recent works on this domain use the prosodic features and the spectrum characteristics of speech signal, with standard(More)
Emotional speech recognition is a multidisciplinary research area that has received increasing attention over the last few years. The present paper considers the application of restricted Boltzmann machines (RBM) and deep belief networks (DBN) to the difficult task of automatic Spanish emotional speech recognition. The principal motivation lies in the(More)
By means of full wavelet packet decomposition a redundant set of coefficients is obtained. For signal classification it is convenient to find a subset of these coefficients minimizing the error rate of a classifier. A problem arises because of the computational cost of GA solution. This work presents the parallelization of a genetic algorithm by which it is(More)
The main objective of the emotion recognition systems is to improve the humanmachine interaction, giving them a more natural behavior to attend different situations and user requirement. Several works on this domain use the prosodic features and the spectrum characteristics of speech signal with classifiers based on neural networks, Gaussian mixtures and(More)
The pervasive development disorders in autism condition lead to impairments in language and social communication. They are evidenced as atypical prosody production, emotion recognition and apraxia, among others communication deficits. This work tackle with the problem of the recognition of pathologies derived from these disorders in children, based on the(More)
The recognition of the emotional states of speaker is a multi-disciplinary research area that has received great interest in the last years. One of the more important goals is to improve the voiced-based human-machine interactions. Recent works on this domain use the prosodic features and the spectrum characteristics of speech signal, with standard(More)
Spoken emotion recognition is a multidisciplinary research area that has received increasing attention over the last few years. In this paper, restricted Boltzmann machines and deep belief networks are used to classify emotions in speech. The motivation lies in the recent success reported using these alternative techniques in speech processing and speech(More)
Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to(More)