Abstract
In the last years, many steps forward have been made in speech and natural languages recognition and were developed many virtual assistants such as Apple’s Siri, Google Now and Microsoft Cortana. Unfortunately, not everyone can use voice to communicate to other people and digital devices. Our system is a first step for extending the possibility of using virtual assistants to speech impaired people by providing an artificial sign languages recognition based on neural network architecture.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
American Sign Language Image Dataset: http://vlm1.uta.edu/%7Esrujana/ASLID/ASL_Image_Dataset.html.
- 2.
Kinect for Windows SDK 2.0: https://www.microsoft.com/en-us/download/details.aspx?id=44561.
- 3.
MSDN Library - “Coordinate mapping”: https://msdn.microsoft.com/it-it/library/dn785530.aspx.
- 4.
Official Keras documentation: https://keras.io/.
- 5.
HandSpeak ASL Dictionary: http://www.handspeak.com/word/.
References
Bellugi, U., Fischer, S.: A comparison of sign language and spoken language. Cognition 1(2), 173–200 (1972)
Pigou, L., Dieleman, S., Kindermans, P.-J., Schrauwen, B.: Sign language recognition using convolutional neural networks. In: Workshop at the European Conference on Computer Vision, pp. 572–578. Springer (2014)
Oz, C., Leu, M.C.: American sign language word recognition with a sensory glove using artificial neural networks. Eng. Appl. Artif. Intell. 24(7), 1204–1213 (2011)
Cooper, H., Ong, E.-J., Pugeault, N., Bowden, R.: Sign language recognition using sub-units. J. Mach. Learn. Res. 13, 2205–2231 (2012)
Gattupalli, S., Ghaderi, A., Athitsos, V.: Evaluation of deep learning based pose estimation for sign language recognition. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 12. ACM (2016)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5(3), 1 (1988)
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)
Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Interspeech, pp. 338–342 (2014)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Park, Y., Moon, S., Suh, I.H.: Tracking human-like natural motion using deep recurrent neural networks (2016). arXiv preprint arXiv:1604.04528
Vella, F., Infantino, I., Scardino, G.: Person identification through entropy oriented mean shift clustering of human gaze patterns. Multimed. Tools Appl. 76(2), 2289–2313 (2017)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Zeiler, M.D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q.V., Nguyen, P., Senior, A., Vanhoucke, V., Dean, J., et al.: On rectified linear units for speech processing. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3517–3521. IEEE (2013)
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, vol. 30 (2013)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Palmeri, M., Vella, F., Infantino, I., Gaglio, S. (2018). Sign Languages Recognition Based on Neural Network Architecture. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-59480-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59479-8
Online ISBN: 978-3-319-59480-4
eBook Packages: EngineeringEngineering (R0)