Hugo Cordeiro

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OBJECTIVES Speech signal processing techniques have provided several contributions to pathologic voice identification, in which healthy and unhealthy voice samples are evaluated. A less common approach is to identify laryngeal pathologies, for which the use of a noninvasive method for pathologic voice identification is an important step forward for(More)
This work investigates the effectiveness of features from the spectral envelope such as the frequency and bandwidth of the first peak obtained from a 30(th) order Linear Predictive Coefficients (LPC) to identify pathological voices. Other spectral features are also investigated and tested to improve the recognition rate. The value of the Relative Power of(More)
This paper proposes two algorithms for the task of 2-speaker unsupervised clustering. The first one creates two SVM models, one for each speaker. The second creates only one SVM model, being each speaker assigned to each class of the same model. These clustering algorithms are based on traditional two-classes SVM and use MLSF coefficients as acoustic(More)
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