Abstract
This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.
Highlights
In the past few decades, medicine has received noteworthy contributions, which have allowed for great advancement in medical activity within various contexts, for example, improvement of surgery techniques, description of the human genome, or even assistance in medical diagnosis
In particular regarding medical diagnosis, digital signal processing techniques have been employed recently as an efficient, noninvasive, and low-cost tool to analyze vocal signals, with the aim of detecting and classifying alterations in the production of sounds that may be associated with larynx pathologies [1]
A method based on MPEG-7 audio low level is proposed in [3] for the extraction of features that can be classified by support vector machine (SVM)
Summary
In the past few decades, medicine has received noteworthy contributions, which have allowed for great advancement in medical activity within various contexts, for example, improvement of surgery techniques, description of the human genome, or even assistance in medical diagnosis. In particular regarding medical diagnosis, digital signal processing techniques have been employed recently as an efficient, noninvasive, and low-cost tool to analyze vocal signals, with the aim of detecting and classifying alterations in the production of sounds that may be associated with larynx pathologies [1]. Most of the techniques involving the analysis of dysfunctions available in the literature employ several sorts of preprocessing stages in order to extract useful characteristics for the classification of diverted patterns and to obtain performance improvement of classifiers [2,3,4,5,6,7,8,9,10,11]. Various techniques have been proposed for the extraction of signal features in order to improve the performance of automatic pathological speech detection systems. Feature extraction is achieved in [10] by using eight measures derived from the nonlinear dynamic analysis
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