Speech Signal Filters based on Soft Computing Techniques: A Comparison

TL;DR

This paper compares different soft computing techniques like neural networks, fuzzy systems, and genetic algorithms for speech signal filtering, highlighting their superior robustness and accuracy over traditional methods.

Contribution

It provides a comprehensive comparison of soft computing techniques for speech filtering, emphasizing their advantages and hybrid approaches.

Findings

Soft computing techniques outperform traditional methods in speech filtering.

Neural networks, fuzzy systems, and genetic algorithms show high robustness.

Hybrid neuro-fuzzy systems offer additional benefits.

Abstract

The paper presents a comparison of various soft computing techniques used for filtering and enhancing speech signals. The three major techniques that fall under soft computing are neural networks, fuzzy systems and genetic algorithms. Other hybrid techniques such as neuro-fuzzy systems are also available. In general, soft computing techniques have been experimentally observed to give far superior performance as compared to non-soft computing techniques in terms of robustness and accuracy.