Exploring New Parameters and Classifiers for Intelligent Speaker Identification

• Grant number: 09/10403-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: November 01, 2009
• End date: October 31, 2011
• Field of knowledge: Engineering - Biomedical Engineering - Bioengineering
• Principal Investigator: Rodrigo Capobianco Guido
• Grantee: André Augusto Spadotto
• Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 05/00015-1 - SpeechAuth: development of a biometric authentication and comand interpretation system based on speech processing, AP.JP

Abstract

This research project proposes to compare and evaluate recent signal processing techniques for speaker identification. For both steps of the process, i.e., (i) features extraction and (ii) classification, new tools will be used. During the former step, a new tool for similarity analysis, called Discrete Shapelet Transform, will be adopted in addition to the traditional ones, i.e., Discrete Wavelet Transform, Cepstrum, MFCCs, and Fractal Dimension. For the latter step, current systems based on Support Vector Machines, Multilayer Perceptrons, Radial Basis Functions, and Bayesian Classifiers will be compared to two new classifiers: (i) Paraconsistent Complex Neural Networks, which have not yet been used for this purpose, and (ii) Optimum Path Forest, which is completely new for this kind of application.