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» Using clustering comparison measures for speaker recognition
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ICMCS
2006
IEEE
180views Multimedia» more  ICMCS 2006»
13 years 11 months ago
Automatic Speaker Segmentation using Multiple Features and Distance Measures: A Comparison of Three Approaches
This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian Information Criterion (BIC) are tested. The first system investigat...
Margarita Kotti, Luis P. M. Martins, Emmanouil Ben...
TSD
2004
Springer
13 years 10 months ago
A Speaker Clustering Algorithm for Fast Speaker Adaptation in Continuous Speech Recognition
In this paper a speaker adaptation methodology is proposed, which first automatically determines a number of speaker clusters in the training material, then estimates the paramete...
Luis Javier Rodríguez, Inés Torres
ICASSP
2010
IEEE
13 years 5 months ago
Speaker identification by combining MFCC and phase information in noisy environments
In conventional speaker recognition methods based on MFCC, the phase information has been ignored. Recently, we proposed a method that integrated MFCC with the phase information o...
Longbiao Wang, Kazue Minami, Kazumasa Yamamoto, Se...
ICASSP
2010
IEEE
13 years 5 months ago
On the use of speaker superfactors for speaker recognition
We propose a new method to characterize a speaker within the Joint Factor Analysis (JFA) framework. Scoring within the JFA framework can be costly and a new method was proposed to...
Nicolas Scheffer, Robbie Vogt
ICASSP
2009
IEEE
13 years 11 months ago
Comparison of scoring methods used in speaker recognition with Joint Factor Analysis
The aim of this paper is to compare different log-likelihood scoring methods, that different sites used in the latest state-of-the-art Joint Factor Analysis (JFA) Speaker Recognit...
Ondrej Glembek, Lukas Burget, Najim Dehak, Niko Br...