Neural network comparison of speech recognition system using trispectrum analysis in noisy environment

Benyamin Kusumoputro; Adi Triyanto

doi:10.1117/12.444213

5 October 2001 Neural network comparison of speech recognition system using trispectrum analysis in noisy environment

Benyamin Kusumoputro, Adi Triyanto

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444213
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

In this paper, a speech recognition system is developed using higher order statistic (HOS) with its fourth order of crosscorrelation (trispectrum) analysis. To analyze the distribution of the trispectrum data along its two- dimensional representation, we developed an adaptive feature extraction mechanism of the trispectrum speech data based on cascade neural network that consists of SOFM (Self-Organizing Feature Map) and LVQ (Learning Vector Quantization). This cascade neural network is used as an adaptive codebook generation algorithm for determining the feature distribution of the trispectrum speech data. Two types of neural networks, namely back-propagation neural network and probabilistic neural networks, are then used as the pattern classifier of this speech recognition system. Comparison of the recognition system using those neural networks as the classifier is conducted based on sample data with and without Gaussian noise. Experimental result showed that PNN has superior recognition rate compared with that of BPNN, especially when a harsh condition of noise is added to the system.

Citation Download Citation

Benyamin Kusumoputro and Adi Triyanto "Neural network comparison of speech recognition system using trispectrum analysis in noisy environment", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444213

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available