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This work describes a novel methodology to characterize voice diseases by using nonlinear dynamics, considering different complexity measures that are mainly based on the analysis of the time delay embedded space. The feature space is represented with a DHMM and a further transformation of the DHMM states to a hyperdimensional space is performed. The discrimination between healthy and pathological...
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 vowels uttered by 50 People with PD (PPD) and...
In this paper low-frequency analysis is addressed in order to explore components of continuous speech signals, trying to making evident the changes in the spectrum, which could be associated to the tremor in speech of people with Parkinson's disease. Four time-frequency (TF) techniques based on WignerVille distribution (WVD) are used for the characterization of the low frequency content of the speech...
Automatic emotion recognition considering speech signals has attracted the attention of the research community in the last years. One of the main challenges is to find suitable features to represent the affective state of the speaker. In this paper, a new set of features derived from the wavelet packet transform is proposed to classify different negative emotions such as anger, fear, and disgust,...
Monitoring animal species by means of the automatic sound recognition is nowadays a research field of high interest. One of the challenges of this area lies in the segmentation of the species vocalizations. Recordings acquired in natural habitats are contaminated with the sounds emitted by other species and different kinds of background noise. If the data is “clean” a robust segmentation is feasible,...
This paper addresses the problem of the automatic recognition of emotional states from speech recordings, especially those kind of emotions reflecting that the life or the human integrity are at risk. The paper compares the performance of two different systems: one being fed with speech signals recorded directly from the people (whole spectrum) and other one in which the speech signals are recorded...
Emotional states produce physiological alterations in the vocal tract introducing variability in the acoustic parameters of speech. Emotion recognition in speech can be used in human-machine interaction applications, speaker verification, analysis of neurological disorders and psychological diagnostic tools. This paper proposes the use of Mel Frequency Cesptral Coefficients (MFCC) for automatic detection...
Problems in voice production can appear due to functional disorders and laryngeal pathologies. The presence of laryngeal pathologies can causes significant changes in the vibrational patterns of the vocal folds and it is demonstrated that the impact of such pathologies can be reduced through continuous speech therapy. We propose a methodology based on non-parametric cepstral coefficients in Mel and...
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