Joint time-frequency analysis for speech recognition

Authors

Albert James M. Licup ⋅ PH Department of Physics, University of San Carlos
Edcel John L. Salumbides ⋅ PH Department of Physics, University of San Carlos
Bernardino J. Buenaobra ⋅ PH Department of Physics, University of San Carlos
Kees Karremans ⋅ NL Vrije Universiteit Amsterdam, The Netherlands and University of San Carlos, Philippines

Abstract

In this paper we present a system developed to recognize a set of words. The feature vectors used are derived from joint time-frequency analysis of the speech signal. We used a multilayer feed-forward neural network trained with the error back propagation algorithm as the feature classifier. We have investigated the network structure that gives the optimum performance. Using a neural network with two hidden layers the system was able to recognize twenty words with 98% recognition rate.

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Issue

2001: Proceedings of the 19th Samahang Pisika ng Pilipinas Philippine Physics Congress

Article ID

SPP-2001-PP-10

Section

Image and Signal Processing

Published

2001-10-24

How to Cite

[1]

AJM Licup, EJL Salumbides, BJ Buenaobra, and K Karremans, Joint time-frequency analysis for speech recognition, Proceedings of the Samahang Pisika ng Pilipinas 19, SPP-2001-PP-10 (2001). URL: https://proceedings.spp-online.org/article/view/SPP-2001-PP-10.