International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 99 - Issue 13 |
Published: August 2014 |
Authors: N. Shanmugapriya, E. Chandra |
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N. Shanmugapriya, E. Chandra . A Thorough Investigation on Speech Enhancement Techniques for Hearing Aids. International Journal of Computer Applications. 99, 13 (August 2014), 9-12. DOI=10.5120/17432-7957
@article{ 10.5120/17432-7957, author = { N. Shanmugapriya,E. Chandra }, title = { A Thorough Investigation on Speech Enhancement Techniques for Hearing Aids }, journal = { International Journal of Computer Applications }, year = { 2014 }, volume = { 99 }, number = { 13 }, pages = { 9-12 }, doi = { 10.5120/17432-7957 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2014 %A N. Shanmugapriya %A E. Chandra %T A Thorough Investigation on Speech Enhancement Techniques for Hearing Aids%T %J International Journal of Computer Applications %V 99 %N 13 %P 9-12 %R 10.5120/17432-7957 %I Foundation of Computer Science (FCS), NY, USA
In recent years, speech development has become an interesting area in the field of signal processing especially in the applications of hearing aids. In hearing aid applications, the speech enhancement technique has been employed mainly for dipping the additive background noise. During the speech enhancement process, high background noise occurs due to the rapture nature of the speech. In order to overcome this problem, various methods have been employed for increasing the speech quality of hearing aid application. This paper provides an outlook of the speech enhancement techniques in a detailed manner with various speech enhancement algorithms such as Wiener filtering, beamformer, generalized singular value decomposition, transformations, and spectral subtraction technique. These methods have been designed with the main aim of reducing background noise. This paper discussed the merits and demerits of all speech enhancement algorithms with their unique characteristic features. The performance of the algorithm has been evaluated with the help of parameter metrics such as PSEQ and SNR.