Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2099
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dc.contributor.authorLe, P.N.
dc.contributor.authorAmbikairajah, E.
dc.contributor.authorThiruvaran, T.
dc.contributor.authorNguyen, T.T.
dc.date.accessioned2021-03-19T04:46:17Z
dc.date.accessioned2022-06-27T10:02:30Z-
dc.date.available2021-03-19T04:46:17Z
dc.date.available2022-06-27T10:02:30Z-
dc.date.issued2015
dc.identifier.citationLe, P. N., Ambikairajah, E., Thiruvaran, T., & Nguyen, T. T. (2015, October). A study of effectiveness of speech enhancement for cognitive load classification in noisy conditions. In 2015 International Conference on Advanced Technologies for Communications (ATC) (pp. 451-455). IEEE.en_US
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2099-
dc.description.abstractIn the last decade, speech-features have been effectively utilized for estimating cognitive load level in ideal conditions where recorded speech is clean. However, in more realistic conditions, the recorded speech data is corrupted by noise. Hence, the employment of speech enhancement is essential to reduce the noise. In this paper, the effectiveness of three speech enhancement algorithms proposed in our previous studies are compared based on performance and processing time and the most suitable method is utilized to denoise the input noisy speech before feeding it to a cognitive load classification system in order to improve its performance. The results of this study indicate that the use of speech enhancement can reduce 3.0% of average relative error rate for the system under the effect of various noisy conditions.en_US
dc.language.isoenen_US
dc.subjectcognitive loaden_US
dc.subjectcccnoisy conditionsen_US
dc.titleA Study of Effectiveness of Speech Enhancement for Cognitive Load Classification in Noisy Conditionsen_US
dc.typeArticleen_US
Appears in Collections:Electrical & Electronic Engineering

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