P. Gomez, “A Text Independent Speaker Recognition System Using a Novel Parametric Neural Network,” Proceedings of International Journal of Signal Processing, Image Processing and Pattern Recognition, December 2011, pp.1-16.

has been cited by the following article:

• TITLE: Text Independent Automatic Speaker Recognition System Using Mel-Frequency Cepstrum Coefficient and Gaussian Mixture Models

  AUTHORS: Alfredo Maesa, Fabio Garzia, Michele Scarpiniti, Roberto Cusani

  KEYWORDS: Automatic Speaker Recognition; Access Control; Voice Recognition; Biometrics

  JOURNAL NAME: Journal of Information Security, Vol.3 No.4, October 31, 2012

  ABSTRACT: The aim of this paper is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture Models (GMM), in order to develop a security control access gate. 450 speakers were randomly extracted from the Voxforge.org audio database, their utterances have been improved using spectral subtraction, then MFCC were extracted and these coefficients were statistically analyzed by GMM in order to build each profile. For each speaker two different speech files were used: the first one to build the profile database, the second one to test the system performance. The accuracy achieved by the proposed approach is greater than 96% and the time spent for a single test run, implemented in Matlab language, is about 2 seconds on a common PC.