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Signal processing techniques can be used to extract information that contribute to the detection of laryngeal disorders. The goal of this paper is to perform a statistical analysis through the boxplot tool from 832 voice signals of individuals with different laryngeal pathologies from the Saarbrücken Voice Database in order to create relevant groups, making feasible an automatic identification of these dysfunctions. Jitter, Shimmer, HNR, NHR and Autocorrelation features were compared between several groups of voice pathologies/conditions, resulting in three identified clusters.

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