Journal of Computer Science


Sundaradhas Selva Nidhyananthan and Ramapackiam Shantha Selva Kumari

DOI : 10.3844/jcssp.2014.178.189

Journal of Computer Science

Volume 10, Issue 1

Pages 178-189


This article evaluates the performance of Extreme Learning Machine (ELM) and Gaussian Mixture Model (GMM) in the context of text independent Multi lingual speaker identification for recorded and synthesized speeches. The type and number of filters in the filter bank, number of samples in each frame of the speech signal and fusion of model scores play a vital role in speaker identification accuracy and are analyzed in this article. Extreme Learning Machine uses a single hidden layer feed forward neural network for multilingual speaker identification. The individual Gaussian components of GMM best represent speaker-dependent spectral shapes that are effective in speaker identity. Both the modeling techniques make use of Linear Predictive Residual Cepstral Coefficient (LPRCC), Mel Frequency Cepstral Coefficient (MFCC), Modified Mel Frequency Cepstral Coefficient (MMFCC) and Bark Frequency Cepstral Coefficient (BFCC) features to represent the speaker specific attributes of speech signals. Experimental results show that GMM outperforms ELM with speaker identification accuracy of 97.5% with frame size of 256 and frame shift of half of frame size and filter bank size of 40.


© 2014 Sundaradhas Selva Nidhyananthan and Ramapackiam Shantha Selva Kumari. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.