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Master's Dissertation
DOI
https://doi.org/10.11606/D.76.2015.tde-16042015-104351
Document
Author
Full name
William Habaro Ishizawa
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2015
Supervisor
Committee
Guido, Rodrigo Capobianco (President)
Carlson Filho, Carlos Magnus
Yehia, Hani Camille
Title in Portuguese
Análise das concentrações energéticas no limiar entre fonemas vozeados e não-vozeados e suas implicações para fins de reconhecimento de locutores dependente do discurso
Keywords in Portuguese
Acústica
Escala Bark
Processamento de sinais
Reconhecimento de locutor
Abstract in Portuguese
Atualmente, diversos trabalhos e aplicações são desenvolvidos com foco na área de reconhecimento computacional de locutores. À medida que o interesse por diversas aplicações reais dentro dessa área emerge, principalmente em biometria, na qual a segurança e a eficácia são de extrema importância, torna-se cada vez mais necessário que estudos sejam feitos, na mesma proporção, visando avaliá-las. Desse modo, a proposta do presente trabalho é a de mensurar a acurácia de um sistema de reconhecimento de locutores baseado em características elementares, isto é, energias de sub-bandas de frequências, em associação com um classificador probabilístico, estudando a viabilidade de extraí-las das transições entre trechos vozeados e não-vozeados (TTVNV) dos sinais. Testes são realizados com diferentes quantidades de locutores e discurso fixado. A acurácia obtida nos testes variam de 20.18% a 92.53%. Os resultados obtidos são comparados e relatados, complementando as afirmações existentes na literatura sobre o uso das TTVNV com dados quantitativos.
Title in English
Analysis of energy cocentrations in the threshold between voiced and unvoiced phonemes and their implications for text-dependent speaker recognition
Keywords in English
Acoustics
Bark scale
Signal processing
Speaker recognition
Abstract in English
Nowadays, many works and applications are developed focusing on computational speaker recognition. As the interest for several real applications within this area emerges, especially in biometrics, where the safety and the efficacy of the applications are extremely important, studies need to be developed in the same proportion, to evaluate the effectiveness of such approaches. Based on that, this work intends to measure the accuracy of a speaker recognition system that uses elementar features, i.e., sub-band frequency energies, associated with a probabilistic classifier, studying the viability of extracting them from the transition between voiced and unvoiced speech tags (TTVNV). Tests are carried out with different numbers of speakers and a text-dependent approach. The accuracy of the tests varies from 20.18% to 92.53%. The results are compared and reported, complementing the existent information on the use of TTVNV with quantitative data.
 
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Publishing Date
2015-04-22
 
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