Speaker recognition with a MLP classifier and LPCC codebook
Sound
2022-03-23 v1 Audio and Speech Processing
Abstract
This paper improves the speaker recognition rates of a MLP classifier and LPCC codebook alone, using a linear combination between both methods. In simulations we have obtained an improvement of 4.7% over a LPCC codebook of 32 vectors and 1.5% for a codebook of 128 vectors (error rate drops from 3.68% to 2.1%). Also we propose an efficient algorithm that reduces the computational complexity of the LPCC-VQ system by a factor of 4.
Cite
@article{arxiv.2203.11614,
title = {Speaker recognition with a MLP classifier and LPCC codebook},
author = {Daniel Rodriguez-Porcheron and Marcos Faundez-Zanuy},
journal= {arXiv preprint arXiv:2203.11614},
year = {2022}
}
Comments
4 pages, published in 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) Phoenix, AZ, USA