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Journal of Cognitive Human-Computer Interaction

ISSN
Online: 2771-1463 Print: 2771-1471
Frequency

Continuous publication

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Open access journal. All articles are freely available online with no APC.

Journal of Cognitive Human-Computer Interaction
Full Length Article

Volume 3Issue 1PP: 36-41 • 2022

An Approach for Devising Stenography Application Using Cross Modal Attention

Shanthalakshmi M. 1* ,
Susmita Mishra 1 ,
LincyJemina S. 1 ,
Raashmi P. 1 ,
Mannuru Shalin 1 ,
Jananeee V. 1
1Rajalakshmi Engineering College, Panimalar Institute of Technology, India
* Corresponding Author.
Received: January 15, 2022 Accepted: May 26, 2022

Abstract

This paper focuses on providing a solution to the direct conversion of speech to shorthand. Since shorthand is not understood by many but is used for writing quick transcripts, a product is developed that converts the speech to its appropriate Gregg shorthand. A website that will be used as a front end, will use a speech-to-text API to record the speech in real-time. The converted text will then be fed into a text-to-image retrieval model that derives its corresponding Gregg shorthand for the text. The text will then be displayed to the user in real-time. By achieving this, the model reduces the need to depend upon stenographers for transcribing scripts. The resulting model achieves a good result.

Keywords

Devising Stenography Cross Modal Attention Speech shorthand Speech conversion

References

[1] DionisA. Padilla, Nicole Kim U. Vitug and Julius Benito S. Marquez., “Deep learning approach in Gregg shorthand word to English word conversion” (2020)

[2] ZhongJi and Kexin Chen, “Step-Wise Hierarchical Alignment Network for Image-Text Matching ’’ (2021)

[3] Xing Xu, Tan Wang, Yang Yang, Lin Zuo, FuminShen, and Heng Tao Shen, “Cross Model Attention with Semantic Consistence for Image Text Matching’’ (2020)

[4] Neha Sharma andShipraSardana, “A Real-Time Speech to Text Conversion system using Bidirectional Kalman Filter Matlab’’(2016)

[5] Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu and Xiaodong He, ”Stacked Cross Attention for Image-Text Matching” (2018)

[6] K. R. Abhinand and H. K. AnasuyaDevi,“An Approach for Generating Pattern-Based Shorthand Using Speech-to-Text Conversion and Machine Learning ’’ (2013)

[7] R.Rajasekaran , K.Ramar, “Handwritten Gregg Shorthand Recognition’’ in International Journal of Computer Applications (2012)

[8] Zihao Wang , Xihui Liu , Hongsheng Li , Lu Sheng , JunjieYan , Xiaogang Wang and Jing Shao, “CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval’’ in IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

[9] StanislavFrolov , Tobias Hinz , Federico Raue , J¨ornHees and Andreas Dengel, “Adversarial Text-to- Image Synthesis: A Review” (Neural Networks Journal,2021)

[10] SaifuddinHitawala, “Comparative Study on Generative Adversarial Networks’’(2018)

[11] Cheng Wang, Haojin Yang, Christian Bartz and ChristophMeinel, “Image Captioning with Deep Bidirectional LSTMs’’ (2016)

[12] Daniela Onita , Adriana Birlutiu and Liviu P. Dinu, “Towards Mapping Images to Text Using Deep- Learning Architectures’’ (2020)

[13] Christine Dewi , Rung-Ching Chen , Yan-Ting Liu and Hui Yu , " Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation'' , (2021)

[14] Hao Wu , Jiayuan Mao , Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, and Wei-Ying Ma., "Unified Visual-Semantic Embeddings: Bridging Vision and Language with Structured Meaning Representations'' , (2019)

[15] Scott Reed, ZeynepAkata, Xinchen Yan, LajanugenLogeswaran , BerntSchiele and Honglak Lee, "Generative Adversarial Text to Image Synthesis'' , (2016)

Cite This Article

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M., Shanthalakshmi, Mishra, Susmita, S., LincyJemina, P., Raashmi, Shalin, Mannuru, V., Jananeee. "An Approach for Devising Stenography Application Using Cross Modal Attention." Journal of Cognitive Human-Computer Interaction, vol. Volume 3, no. Issue 1, 2022, pp. 36-41. DOI: https://doi.org/10.54216/JCHCI.030105
M., S., Mishra, S., S., L., P., R., Shalin, M., V., J. (2022). An Approach for Devising Stenography Application Using Cross Modal Attention. Journal of Cognitive Human-Computer Interaction, Volume 3(Issue 1), 36-41. DOI: https://doi.org/10.54216/JCHCI.030105
M., Shanthalakshmi, Mishra, Susmita, S., LincyJemina, P., Raashmi, Shalin, Mannuru, V., Jananeee. "An Approach for Devising Stenography Application Using Cross Modal Attention." Journal of Cognitive Human-Computer Interaction Volume 3, no. Issue 1 (2022): 36-41. DOI: https://doi.org/10.54216/JCHCI.030105
M., S., Mishra, S., S., L., P., R., Shalin, M., V., J. (2022) 'An Approach for Devising Stenography Application Using Cross Modal Attention', Journal of Cognitive Human-Computer Interaction, Volume 3(Issue 1), pp. 36-41. DOI: https://doi.org/10.54216/JCHCI.030105
M. S, Mishra S, S. L, P. R, Shalin M, V. J. An Approach for Devising Stenography Application Using Cross Modal Attention. Journal of Cognitive Human-Computer Interaction. 2022;Volume 3(Issue 1):36-41. DOI: https://doi.org/10.54216/JCHCI.030105
S. M., S. Mishra, L. S., R. P., M. Shalin, J. V., "An Approach for Devising Stenography Application Using Cross Modal Attention," Journal of Cognitive Human-Computer Interaction, vol. Volume 3, no. Issue 1, pp. 36-41, 2022. DOI: https://doi.org/10.54216/JCHCI.030105
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