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Data mining applied to forensic speaker identification
- Publication Year :
- 2015
- Publisher :
- Institute of Electrical and Electronics Engineers, 2015.
-
Abstract
- 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. Fil: Univaso, Pedro Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina Fil: Ale, Juan Maria. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina Fil: Gurlekian, Jorge Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina
- Subjects :
- ENSEMBLE METHODS
General Computer Science
Computer science
Speech recognition
computer.software_genre
Logistic model tree
DATA MINING
Information gain ratio
DATA FUSION
Electrical and Electronic Engineering
CLASSIFIERS
Otras Ciencias de la Computación e Información
business.industry
Pattern recognition
Speaker recognition
Ensemble learning
Random subspace method
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Multilayer perceptron
Ciencias de la Computación e Información
SPEAKER RECOGNITION
Data mining
Artificial intelligence
business
computer
LogitBoost
CIENCIAS NATURALES Y EXACTAS
Subjects
Details
- Language :
- Spanish; Castilian
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....98ddfb9b03a6ebcdc68a1dffdcbd07fb