Comparison of spectral analysis models for feature extraction in word recognition

Authors

Albert James M. Licup Department of Physics, University of San Carlos
Marlo A. Flores Department of Physics, University of San Carlos
Edcel John L. Salumbides Department of Physics, University of San Carlos
Kees Karremans 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.

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Issue

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

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.