{"title":"A Customized Artificial Ear Based on Vibrotactile Feedback: A Pilot Study","authors":"Yicheng Yang, Weibang Bai, Benny P. L. Lo","doi":"10.1109/BSN56160.2022.9928488","DOIUrl":null,"url":null,"abstract":"Hearing aid devices have been around for decades, while most of them focus on sound amplification and SNR improvement. This paper proposes an artificial ear based on the vibrotactile feedback. The speech signal is converted into the vibrotactile devices placed around the subject’s ear through the speech recognition algorithm and pattern coding method. Preliminary experiments on the prototype consisting of six motors which has shown that the recognition accuracy of letters and daily sentences reached 90%. The learning time of interpreting the vibrotactile signals could be less than four times that in real-time conversation, proving the feasibility of the proposed device for real-life application.","PeriodicalId":150990,"journal":{"name":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN56160.2022.9928488","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
Hearing aid devices have been around for decades, while most of them focus on sound amplification and SNR improvement. This paper proposes an artificial ear based on the vibrotactile feedback. The speech signal is converted into the vibrotactile devices placed around the subject’s ear through the speech recognition algorithm and pattern coding method. Preliminary experiments on the prototype consisting of six motors which has shown that the recognition accuracy of letters and daily sentences reached 90%. The learning time of interpreting the vibrotactile signals could be less than four times that in real-time conversation, proving the feasibility of the proposed device for real-life application.