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2021 | OriginalPaper | Chapter

Deep Learning-Based Computer Aided Customization of Speech Therapy

Authors : Sarthak Agarwal, Vaibhav Saxena, Vaibhav Singal, Swati Aggarwal

Published in: Applications of Artificial Intelligence and Machine Learning

Publisher: Springer Singapore

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Abstract

Video frame interpolation is a computer vision technique used to synthesize intermediate frames between two subsequent frames. This technique has been extensively used for the purpose of video upsampling, video compression and video rendering. We present here an unexplored application of frame interpolation, by using it to join different phoneme videos in order to generate speech videos. Such videos can be used for the purpose of speech entrainment, as well as help to create lip reading video exercises. We propose an end-to-end convolutional neural network employing a U-net architecture that learns optical flows and generates intermediate frames between two different phoneme videos. The quality of the model is evaluated against qualitative measures like the Structural Similarity Index (SSIM) and the peak signal-to-noise ratio (PSNR), and performs favorably well, with an SSIM score of 0.870, and a PSNR score of 33.844.

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Literature
1.
go back to reference Fridriksson J, Hubbard HI, Hudspeth SG, Holland AL, Bonilha L, Fromm D, Rorden C (2012) Speech entrainment enables patients with Broca’s Aphasia to produce fluent speech. Brain J Neurol Fridriksson J, Hubbard HI, Hudspeth SG, Holland AL, Bonilha L, Fromm D, Rorden C (2012) Speech entrainment enables patients with Broca’s Aphasia to produce fluent speech. Brain J Neurol
2.
go back to reference Zhai J, Yu K, Li J, Li S (2005) A low complexity motion compensated frame interpolation method. In: Proceedings of IEEE international symposium on circuits and systems 2005 Zhai J, Yu K, Li J, Li S (2005) A low complexity motion compensated frame interpolation method. In: Proceedings of IEEE international symposium on circuits and systems 2005
3.
go back to reference Ha T, Lee S, Kim J (2004) Motion compensated frame interpolation by new block-based motion estimation algorithm. IEEE Trans Consum Electron Ha T, Lee S, Kim J (2004) Motion compensated frame interpolation by new block-based motion estimation algorithm. IEEE Trans Consum Electron
4.
go back to reference Guo D, Lu Z (2016) The grid: motion-compensated frame interpolation with weighted motion estimation and hierarchical vector refinement. Neurocomputing Guo D, Lu Z (2016) The grid: motion-compensated frame interpolation with weighted motion estimation and hierarchical vector refinement. Neurocomputing
5.
go back to reference Liu Z, Yeh R, Tang X, Liu Y, Agarwala A (2017) Video frame synthesis using deep voxel flow. In: ECCV 2017 Liu Z, Yeh R, Tang X, Liu Y, Agarwala A (2017) Video frame synthesis using deep voxel flow. In: ECCV 2017
6.
go back to reference Sharma A, Menda K, Koren M (2017) Convolutional neural networks for video frame interpolation. Neurocomputing Sharma A, Menda K, Koren M (2017) Convolutional neural networks for video frame interpolation. Neurocomputing
7.
go back to reference Jiang H, Sun D, Jampani V, Yang MH, Miller EL, Kautz J (2017) Super SloMo: high quality estimation of multiple intermediate frames for video. Interpolation. arXiv:1712.00080v1 [cs.CV] 2017 Jiang H, Sun D, Jampani V, Yang MH, Miller EL, Kautz J (2017) Super SloMo: high quality estimation of multiple intermediate frames for video. Interpolation. arXiv:​1712.​00080v1 [cs.CV] 2017
8.
go back to reference Niklaus S, Mai L, Liu F (2017) Video frame interpolation via adaptive convolution. In: CVPR 2017 Niklaus S, Mai L, Liu F (2017) Video frame interpolation via adaptive convolution. In: CVPR 2017
9.
go back to reference Long G, Kneip L, Alvarez JM, Li H, Zhang X, Yu Q (2016) Learning image matching by simply watching video. In: ECCV 2016 Long G, Kneip L, Alvarez JM, Li H, Zhang X, Yu Q (2016) Learning image matching by simply watching video. In: ECCV 2016
10.
11.
go back to reference Yahia HB, Frame interpolation using convolutional neural networks on 2D animation. MA thesis, University of Amsterdam, Amsterdam, The Netherland Yahia HB, Frame interpolation using convolutional neural networks on 2D animation. MA thesis, University of Amsterdam, Amsterdam, The Netherland
12.
go back to reference Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res
13.
go back to reference Zhao H, Gallo O, Frosio I, Kautz J (2016) Loss functions for neural networks for image processing. IEEE Trans Comput Imaging Zhao H, Gallo O, Frosio I, Kautz J (2016) Loss functions for neural networks for image processing. IEEE Trans Comput Imaging
14.
go back to reference Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process
15.
go back to reference Horé A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: International conference on pattern recognition (ICPR) 2010 Horé A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: International conference on pattern recognition (ICPR) 2010
16.
go back to reference Sharma A, Menda K, Koren M (2017) Frame interpolation using generative adversarial networks. Neurocomputing Sharma A, Menda K, Koren M (2017) Frame interpolation using generative adversarial networks. Neurocomputing
17.
go back to reference Amersfoort J, Shi W, Acosta A, Massa F, Totz J, Wang Z, Caballero J (2017) Frame interpolation with multi-scale deep loss functions and generative adversarial networks. arXiv:1711.06045v1 [cs.CV] 2017 Amersfoort J, Shi W, Acosta A, Massa F, Totz J, Wang Z, Caballero J (2017) Frame interpolation with multi-scale deep loss functions and generative adversarial networks. arXiv:​1711.​06045v1 [cs.CV] 2017
19.
go back to reference Guilliams I, Segui A (1988) Interactive videodisc for teaching and evaluating lipreading. Eng Med Biol Soc Guilliams I, Segui A (1988) Interactive videodisc for teaching and evaluating lipreading. Eng Med Biol Soc
Metadata
Title
Deep Learning-Based Computer Aided Customization of Speech Therapy
Authors
Sarthak Agarwal
Vaibhav Saxena
Vaibhav Singal
Swati Aggarwal
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-16-3067-5_36

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