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Speech Rate Control for Improving Elderly Speech Recognition of Smart DevicesSON, G. , KWON, S. , LIM, Y.
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automatic speech recognition, human computer interaction, speech analysis, man machine systems, human factor
speech(15), time(4), communication(4), aging(4)
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About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 79 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02011
Web of Science Accession Number: 000405378100011
SCOPUS ID: 85020117598
Although smart devices have become a widely-adopted tool for communication in modern society, it still requires a steep learning curve among the elderly. By introducing a voice-based interface for smart devices using voice recognition technology, smart devices can become more user-friendly and useful to the elderly. However, the voice recognition technology used in current devices is attuned to the voice patterns of the young. Therefore, speech recognition falters when an elderly user speaks into the device. This paper has identified that the elderly's improper speech rate by each syllable contributes to the failure in the voice recognition system. Thus, upon modifying the speech rate by each syllable, the voice recognition rate saw an increase of 12.3%. This paper demonstrates that by simply modifying the speech rate by each syllable, which is one of the factors that causes errors in voice recognition, the recognition rate can be substantially increased. Such improvements in voice recognition technology can make it easier for the elderly to operate smart devices that will allow them to be more socially connected in a mobile world and access information at their fingertips. It may also be helpful in bridging the communication divide between generations.
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 Fuzzy Integral and Cuckoo Search Based Classifier Fusion for Human Action Recognition, AYDIN, I., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 18, 2018.
Digital Object Identifier: 10.4316/AECE.2018.01001 [CrossRef] [Full text]
 DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software, Wang, Yang Yang, Gao, Ke, Kloepper, Ashley M., Zhao, Yunxin, Kuruvilla-Dugdale, Mili, Lever, Teresa E., Bunyak, Filiz, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), ISBN 978-1-7281-0848-3, 2019.
Digital Object Identifier: 10.1109/BHI.2019.8834506 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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