5565634
doi
10.35940/ijeat.D7911.049420
oai:zenodo.org:5565634
Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)
Publisher
Hemant Singh
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Animesh Mohanty
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Real-Time Speech-To-Text / Text-To-Speech Converter With Automatic Text Summarizer using Natural Language Generation And Abstract Meaning Representation
K. P. Vijayakumar
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Sequence to Sequence models, Neural Networks, Deep Speech 2, AMR parsing, Batch Normalization, SortaGrad, NLP, NLG, CTC.
<p>Due to extensive needs for growth in various sectors, which include software, telecom, healthcare, defence, etc., there is a necessary increase in the number as well as the duration of meetings, conference calls, reconnaissance stakeouts, financial reviews. The obtained reports of these play a significant role in defining the plan of actions. The proposed model is to convert real-time speech to corresponding text and then to its respective summary using Natural Language Grammar (NLG) and Abstract Meaning Representation (AMR) graphs and then again turned back the obtained summary to speech. The proposed model intends to achieve the task using two major algorithms, 1) Deep Speech 2, 2) AMR graphs. The speech-recognition model recommended has a speedup of 4x if the algorithm runs on a Central Processing Unit (CPU), and the use of particular Graphics Processing Units (GPUs) for running deep learning algorithms can give a speedup of 21x. The performance of the summarizer used is close to the Lead-3-AMR-Baseline model, which is a solid baseline for the CNN/Dailymail dataset. The summarizer we use scores ROGUE score close to the Lead-3- AMR-Baseline model with an accuracy of 99.37%.</p>
Zenodo
2020-04-30
info:eu-repo/semantics/article
5565633
1634176113.780962
737116
md5:81ffaa718d5277ec2bf69aa3eea205fe
https://zenodo.org/records/5565634/files/D7911049420 (1).pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
9
4
2361-2365
2020-04-30