Elkyn Alexander Belalcázar-Bolaños

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In this paper, the analysis of low-frequency zone of the speech signals from the five Spanish vowels, by means of the Teager energy operator (TEO) and the modified group delay functions (MGDF) is proposed for the automatic detection of Parkinson’s disease. According to our findings, different implementations of the TEO are suitable for tackling the problem(More)
Parkinson’s disease (PD) is a chronic neurodegenerative disorder of the nervous central system and it can affect the communication skills of the patients. There is an interest in the research community to develop computer aided tools for the analysis of the speech of people with PD for detection and monitoring. In this paper, three new acoustic measures for(More)
This paper evaluates the accuracy of different characterization methods for the automatic detection of multiple speech disorders. The speech impairments considered include dysphonia in people with Parkinson's disease (PD), dysphonia diagnosed in patients with different laryngeal pathologies (LP), and hypernasality in children with cleft lip and palate(More)
In this paper we propose a methodology for the automatic detection of Parkinson’s Disease (PD) by using several glottal flow measures including different time-frequency (TF) parameters and nonlinear behavior of the vocal folds. Additionally, the nonlinear behavior of the vocal tract is characterized using the residual wave. The proposed approach allows(More)
As a methodology for automatic detection of Parkinson's disease (PD), it is proposed the estimation of the different glottal flow features considering nonlinear behavior of the vocal folds. This paper evaluates the discrimination capability of set with eight different Nonlinear Dynamic (NLD) features. The experiment presented considering the five Spanish(More)
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