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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM13631

IDENTIFICATION OF AMERICAN SIGN LANGUAGE TO SPEECH CONVERSION USING TRANSFER LEARNING (SHUFFELNET)

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM13631

Volume:

6 |

5 |

May 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM13631

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VJ

Vijay J

VJ

Vijayakumar J

MPM

Mervin Paulraj M

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Abstract

Sign language is used to facilitate communication between normal people and deaf and hard of hearing people. Many people are unaware of sign language, which creates a barrier to knowledge sharing and comprehension of thoughts and feelings. The convolution neural network is used in this study to train sign languages using the ShuffleNet transfer learning model. During training, the network accuracy is 99.89 percent and the testing accuracy is 84.76 percent. Matlab’s converter is used to translate the output (Text to Speech).

KEYWORDS

Sign Language Deep Learning Transfer Learning ShuffleNet Neural Network.

Cite This Article

Vijay J, Vijayakumar J, Mervin Paulraj M, "IDENTIFICATION OF AMERICAN SIGN LANGUAGE TO SPEECH CONVERSION USING TRANSFER LEARNING (SHUFFELNET)," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 5, May 2022. https://doi.org/10.55041/IJSREM13631

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Publication Date

5/25/2022

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The authors declare no conflict of interest related to this research work.

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