Odyssey 2016

In this paper, a traditional clustering algorithm based on speaker identification is presented. Several audio data sets were tested to conclude how accurate the clustering algorithm is depending on the characteristics of the analyzed database. We show that, issues such as the size of the database, the number speakers, or how the audios are balanced over the speakers in the database significantly affect the accuracy of the clustering task. These conclusions can be used to propose strategies to solve a clustering task or to predict in which situations a higher performance of the clustering algorithm is expected. We also focus on the stopping criterion to avoid the worsening of the results due to mismatch between training and testing data while using traditional stopping criteria based on maximum distance thresholds.