Speaking aid using neural networks for the deaf

Mu Chun Su

Speaking aid using neural networks for the deaf

Mu Chun Su

資訊工程學系

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

1 引文斯高帕斯（Scopus）

摘要

In this paper, we propose to use two EMI-Gloves connected to an IBM compatible PC via Degraded HyperEllipsoidal Composite Neural Networks (DHECNNs) to implement a prototype speaking aid for the deaf or non-vocal persons. Using a special learning scheme, the DHECNNs can quickly learn the complex mapping of measurements of ten fingers' flex angles to corresponding categories. The prototype speaking aid is evaluated for the classification of 51 static hand gestures from 4 'speakers'. The recognition accuracy for the testing set is 92.3%.

原文	???core.languages.en_GB???
頁（從 - 到）	351-357
頁數	7
期刊	Biomedical Engineering - Applications, Basis and Communications
卷	8
發行號	4
出版狀態	已出版 - 1 1月 1996

引用此

@article{6b87bb95e02749e298ac16f0ca467fff,

title = "Speaking aid using neural networks for the deaf",

abstract = "In this paper, we propose to use two EMI-Gloves connected to an IBM compatible PC via Degraded HyperEllipsoidal Composite Neural Networks (DHECNNs) to implement a prototype speaking aid for the deaf or non-vocal persons. Using a special learning scheme, the DHECNNs can quickly learn the complex mapping of measurements of ten fingers' flex angles to corresponding categories. The prototype speaking aid is evaluated for the classification of 51 static hand gestures from 4 'speakers'. The recognition accuracy for the testing set is 92.3%.",

author = "Su, {Mu Chun}",

year = "1996",

month = jan,

day = "1",

language = "???core.languages.en_GB???",

volume = "8",

pages = "351--357",

journal = "Biomedical Engineering - Applications, Basis and Communications",

issn = "1016-2372",

number = "4",

}

TY - JOUR

T1 - Speaking aid using neural networks for the deaf

AU - Su, Mu Chun

PY - 1996/1/1

Y1 - 1996/1/1

N2 - In this paper, we propose to use two EMI-Gloves connected to an IBM compatible PC via Degraded HyperEllipsoidal Composite Neural Networks (DHECNNs) to implement a prototype speaking aid for the deaf or non-vocal persons. Using a special learning scheme, the DHECNNs can quickly learn the complex mapping of measurements of ten fingers' flex angles to corresponding categories. The prototype speaking aid is evaluated for the classification of 51 static hand gestures from 4 'speakers'. The recognition accuracy for the testing set is 92.3%.

AB - In this paper, we propose to use two EMI-Gloves connected to an IBM compatible PC via Degraded HyperEllipsoidal Composite Neural Networks (DHECNNs) to implement a prototype speaking aid for the deaf or non-vocal persons. Using a special learning scheme, the DHECNNs can quickly learn the complex mapping of measurements of ten fingers' flex angles to corresponding categories. The prototype speaking aid is evaluated for the classification of 51 static hand gestures from 4 'speakers'. The recognition accuracy for the testing set is 92.3%.

UR - http://www.scopus.com/inward/record.url?scp=0030206606&partnerID=8YFLogxK

M3 - 期刊論文

AN - SCOPUS:0030206606

VL - 8

SP - 351

EP - 357

JO - Biomedical Engineering - Applications, Basis and Communications

JF - Biomedical Engineering - Applications, Basis and Communications

SN - 1016-2372

IS - 4

ER -

Speaking aid using neural networks for the deaf

摘要

指紋

引用此