P. Le, E. Ambikairajah, Tharmarajah Thiruvaran, T. Nguyen
{"title":"A study of effectiveness of speech enhancement for cognitive load classification in noisy conditions","authors":"P. Le, E. Ambikairajah, Tharmarajah Thiruvaran, T. Nguyen","doi":"10.1109/ATC.2015.7388370","DOIUrl":null,"url":null,"abstract":"In 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.","PeriodicalId":142783,"journal":{"name":"2015 International Conference on Advanced Technologies for Communications (ATC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Advanced Technologies for Communications (ATC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATC.2015.7388370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In 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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
