Comparison of spectral analysis models for feature extraction in word recognition

Authors

Albert James M. Licup ⋅ PH Department of Physics, University of San Carlos
Marlo A. Flores ⋅ PH Department of Physics, University of San Carlos
Edcel John L. Salumbides ⋅ PH Department of Physics, University of San Carlos
Kees Karremans ⋅ NL Vrije Universiteit Amsterdam, The Netherlands

Abstract

In this paper several speech features that could best represent a set of isolated words are compared. Features are derived from the power spectrum, the spectrogram, and the linear predictive code (LPC). Using a k-nearest neighbor classifier, we have obtained best results of 99.8% for the recognition rate when the features were obtained using LPC coefficients.

Downloads

Issue

2002: Proceedings of the 20th Samahang Pisika ng Pilipinas Physics Congress

Ins-FIRE-ing excellence in physics education and research
23-25 October 2002, Ateneo de Naga University

Article ID

SPP-2002-2H-03

Section

Image and Signal Processing

Published

2002-10-23

How to Cite

[1]

AJM Licup, MA Flores, EJL Salumbides, and K Karremans, Comparison of spectral analysis models for feature extraction in word recognition, Proceedings of the Samahang Pisika ng Pilipinas 20, SPP-2002-2H-03 (2002). URL: https://proceedings.spp-online.org/article/view/SPP-2002-2H-03.