Herramientas de minería de datos aplicadas a la identificación de hablantes en el ámbito forense
(Data Mining applied to Forensic Speaker Identification)
Pedro Univaso (firstname.lastname@example.org)2, Juan María Ale (email@example.com)1, Jorge A. Gurlekian (firstname.lastname@example.org)2
1Universidad de Buenos Aires2Laboratorio de Investigaciones Sensoriales
This paper appears in: Revista IEEE América Latina
Publication Date: April 2015
Volume: 13, Issue: 4
In this paper we analyze the advantages of using data mining techniques and tools for data fusion in forensic speaker recognition. Segmental and suprasegmental features were employed in 28 different classifiers, in order to compare their performances. The selected classifiers have different learning techniques: lazy or instance-based, eager and ensemble. Two approaches were employed on the classification task: the use of all features and the use of a feature subset, selected with a gain ratio methodology. The best performances, with all features, were obtained by three classifiers: Logistic Model Tree (eager), LogitBoost (ensemble) and Multilayer Perceptron (eager). Support Vector Machine (eager) proved to be a good classifier if a Pearson VII function-based universal kernel was used. When low dimensional features were selected, ensemble classifiers exceeded the performance of all others classifiers. Segmental and tone features demonstrated the best speaker discrimination capabilities, followed by duration and quality voice features. Evaluation was performed on Argentine-Spanish voice samples from the Speech_Dat database recorded on a fixed telephone environment. Different recording sessions and channels for the test segments were added and the Z-norm procedure was applied for channel compensation.
Data Mining, Classifiers, Ensemble Methods, Speaker Recognition, Data Fusion
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