Authors :
Manasa Mandapati
Volume/Issue :
Volume 10 - 2025, Issue 9 - September
Google Scholar :
https://tinyurl.com/bd5mhemr
Scribd :
https://tinyurl.com/29wznkm4
DOI :
https://doi.org/10.38124/ijisrt/25sep099
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Abstract :
The hearing-impaired people form an ample community with required needs that technologists have started to
address. There is no device to date that can convert audio to sign language and sign language to audio in real-time. This
problem can be handled using an interpreter system for speech to sign language to translate English speech to American
Sign Language video in real time[2]. Similarly the sign language is translated to speech using a device with arduino board and
flex sensors. Gestures made by the wearer are detected using sensors, and as per the pre-defined conditions for numerous values
generated by sensors, corresponding messages were sent to the Android device using Global System for Mobile (GSM) [4],
which will convert these text messages to speech.
Keywords :
Sign Language, Speech Recognition, Flex Sensors, Gestures, Speech to Text, Text to Speech.
References :
- American Sign Language Video Dictionary and Inflection Guide. (2000). [CD-ROM]. New York: US. National Technical Institute for the Deaf, Rochester Institute of technology. ISBN: 0-9720942-0-2
- ASL University. Finferspelling: Introduction. http://www.lifeprint.com/asl101/fingerspelling/fingerspelling.
- Baker, J.K. (1975). The DRAGON System-An Overview. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-23(1). pp.24-29.
- Becchetti, C., Ricotti, L. R. (1999). Speech Recognition Theory and C++ Implementation. England: Wiley.
- Bornstein, H., Saulnier, K.L. Hamilton, L.B. (1992). The Comprehensive Signed English Dictionary (Sixth printing). USA: Washington DC, The Signed English series. Clerc Books, Gallaudet University Pres.
- Gouveˆa, E. The CMU Sphinx Group Open Source Speech Recognition Engines. http://www.speech.cs.cmu.edu/sphinx/
- Harrington, T. (July, 2004). Statistics: Deaf Population of the US. http://library.gallaudet.edu/dr/faq-statistics-deafus.html
- Huang, X., Acero, A., Hon, H-W., Reddy, R. (2001). Spoken Language processing, a Guide to Theory, Algorithm and System Development. Prentice Hall PTR
- Hwang, Mei-Yuh. (1993). Subphonetic Acoustic Modeling for Speaker Independent Continuous Speech Recognition. Ph.D. thesis, Computer Science Department, Carnegie Mellon University. Tech Report No. CMU-CS-93-230
- iCommunicator TM pricing (2003). http://www.myicommunicator.com/?action=pricing
- Jelinek, F. (Apr. 1976). Continuous Speech Recognition by Statistical Methods. Proceedings of the IEEE, Vol. 64, No. 4. pp. 532-556.
- Ravishankar, M. (May 1996) Efficient Algorithms for Speech Recognition. Ph.D. dissertation, Carnegie Mellon University. Tech Report. No. CMU-CS-96 143.
- Ravishankar, M. K. (2004). Sphinx-3 s3.X Decoder (X=5). Sphinx Speech Group. School of Computer Science, CMU. http://cmusphinx.sourceforge.net/sphinx3/
- Rosenfeld, R. The CMU Statistical Language Modeling (SLM) Toolkit, http://www.speech.cs.cmu.edu/SLMi n ƒ o.ktml
The hearing-impaired people form an ample community with required needs that technologists have started to
address. There is no device to date that can convert audio to sign language and sign language to audio in real-time. This
problem can be handled using an interpreter system for speech to sign language to translate English speech to American
Sign Language video in real time[2]. Similarly the sign language is translated to speech using a device with arduino board and
flex sensors. Gestures made by the wearer are detected using sensors, and as per the pre-defined conditions for numerous values
generated by sensors, corresponding messages were sent to the Android device using Global System for Mobile (GSM) [4],
which will convert these text messages to speech.
Keywords :
Sign Language, Speech Recognition, Flex Sensors, Gestures, Speech to Text, Text to Speech.