Speech Signal Filters based on Soft Computing Techniques: A Comparison
This work addresses the problem of improving speech signal processing for applications like communication or audio systems, but it is incremental as it focuses on comparing existing techniques rather than introducing new ones.
The paper compared various soft computing techniques, including neural networks, fuzzy systems, and genetic algorithms, for filtering and enhancing speech signals, finding that these techniques generally offer superior robustness and accuracy compared to non-soft computing methods.
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.