HMM Speaker Identification Using Linear and Non-linear Merging Techniques
Abstract
Speaker identification is a powerful, non-invasive and in-expensive biometric technique. The recognition accuracy, however, deteriorates when noise levels affect a specific band of frequency. In this paper, we present a sub-band based speaker identification that intends to improve the live testing performance. Each frequency sub-band is processed and classified independently. We also compare the linear and non-linear merging techniques for the sub-bands recognizer. Support vector machines and Gaussian Mixture models are the non-linear merging techniques that are investigated. Results showed that the sub-band based method used with linear merging techniques enormously improved the performance of the speaker identification over the performance of wide-band recognizers when tested live. A live testing improvement of 9.78% was achieved
Cite
@article{arxiv.0705.1585,
title = {HMM Speaker Identification Using Linear and Non-linear Merging Techniques},
author = {Unathi Mahola and Fulufhelo V. Nelwamondo and Tshilidzi Marwala},
journal= {arXiv preprint arXiv:0705.1585},
year = {2007}
}