HiTEK Pre-processing for Speech and Text: NLP

Naveenkumar T Rudrappa; Mallamma V Reddy; M Hanumanthappa

doi:10.17485/IJST/v16i19.296

Article

HiTEK Pre-processing for Speech and Text: NLP

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i19.296

Year: 2023, Volume: 16, Issue: 19, Pages: 1413-1421

Original Article

HiTEK Pre-processing for Speech and Text: NLP

Naveenkumar T Rudrappa^1*, Mallamma V Reddy², M Hanumanthappa³

¹Research Scholar, Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India
²Assistant Professor, Department of Computer Science, Rani Channamma University, Belagavi, Karnataka, India
³Professor of Computer Science and Applications, Jnanabharati Campus, Bangalore University, Bangalore, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:14 February 2023, Accepted Date:22 April 2023, Published Date:12 May 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To develop a system that accepts a sentence consisting of two and/or four languages and convert it to a target language text, termed as Cross Language Speech Identification and Text Translation System. Methods: A combinatorial model consisting of Hidden Markov Model, Artificial Neural Networks, Deep Neural Networks and Gaussian Mixture Model are utilized for direct and indirect speech mapping. Trained dataset consisting of thousand phonemes for each of the Hindi, Telugu, English and Kannada languages, initially for bank, hospital domains, later the grammatical phonemes of each language were added and wave files consisting of cross lingual spoken sentence were created which incurred a six months period to build from scratch, as cross lingual vocal data-set is not available. Hindi language dataset Shabdanjali was also referred. The basic parameters considered for creation of structured dataset are loudness, pause, pitch, tone, noise cancellation, sampling frequency, threshold etc. Findings: Comparative analysis of various techniques, target languages and features are tabulated. Research idea emerged from the comparative analysis of Monolingual Systems where there was a gap for cross lingual speech to text translation. The architecture can be enhanced in future for other regional languages of India. Novelty: A new bench mark for Cross Language dataset was created. This work presents CLSITT tool applicable in transforming public speeches spoken in multiple languages to a selected target language and the tool is helpful for a regional news editor, rural and agricultural activities, medical applications, defence and so on.

Keywords: Artificial Intelligence (AI); Deep Learning (DL); Machine Learning (ML); Natural Language Processing (NLP)

References

Patel J, Makvana K, Shah P. Cross-lingual Information Retrieval: application and Challenges for Indian Languages. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). (pp. 1-4) IEEE. 2019.
Shah S, Sitaram S. Using Monolingual Speech Recognition for Spoken Term Detection in Code-switched Hindi-English Speech. In: 2019 International Conference on Data Mining Workshops (ICDMW). (pp. 1-5) IEEE. 2019.
Nagarhalli TP, Mhatre S, Patil S, Patil P. The Review of Natural Language Processing Applications with Emphasis on Machine Learning Implementations. 2022 International Conference on Electronics and Renewable Systems (ICEARS). 2022;p. 1353–1358. Available from: https://doi.org/10.1109/ICEARS53579.2022.9752326
Khurana D, Koli A, Khatter K, Singh S. Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications. 2023;82(3):3713–3744. Available from: https://doi.org/10.1007/s11042-022-13428-4
Banane M, Erraissi A. A comprehensive study of Natural Language processing techniques Based on Big Data. 2022 International Conference on Decision Aid Sciences and Applications (DASA). 2022;p. 1492–1497. Available from: https://doi.org/10.1109/DASA54658.2022.9765270
Saini S, Sahula V. A Survey of Machine Translation Techniques and Systems for Indian Languages. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology. (pp. 676-681) IEEE. 2015.
Vyas R, Joshi K, Sutar H, Nagarhalli TP. Real Time Machine Translation System for English to Indian language. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). (pp. 838-842) IEEE. 2020.
Satpathy S, Mishra SP, Nayak AK. Analysis of Learning Approaches for Machine Translation Systems. In: 2019 International Conference on Applied Machine Learning (ICAML). (pp. 160-164) IEEE. 2019.
Sohail AS, Sameen M, Ahmed Q. Formal Notations of Linguistic Analysis for Monetary Policy. In: 2019 International Conference on Green and Human Information Technology (ICGHIT). (pp. 119-121) IEEE. 2019.
Hanif A, Zhang X, Wood S. A Survey on Explainable Artificial Intelligence Techniques and Challenges. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW). (pp. 81-89) IEEE. 2021.
Durga S, Nag R, Daniel E. Survey on Machine Learning and Deep Learning Algorithms used in Internet of Things (IoT) Healthcare. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). (pp. 1018-1022) IEEE. 2019.
Jiang K, Lu X. Natural Language Processing and Its Applications in Machine Translation: A Diachronic Review. In: 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). (pp. 210-214) IEEE. 2020.
Zhang X, Peng Y, Xu X. An Overview of Speech Recognition Technology. In: 2019 4th International Conference on Control, Robotics and Cybernetics (CRC). (pp. 81-85) IEEE. 2019.
Jaiswal RK, Dubey RK. Concatenative Text-to-Speech Synthesis System for Communication Recognition. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA). (pp. 867-872) IEEE. 2021.
Patel P, Singh NC, Torppa M. Understanding the role of cross-language transfer of phonological awareness in emergent Hindi–English biliteracy acquisition. Reading and Writing. 2022. Available from: https://doi.org/10.1007/s11145-022-10253-x
Kanan T, Sadaqa O, Aldajeh A, Alshwabka H, Al-Dolime W, Alzu'bi S, et al. A Review of Natural Language Processing and Machine Learning Tools Used to Analyze Arabic Social Media. In: 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). (pp. 622-628) IEEE. 2019.
Azkan C, Moller F, Meisel L, Otto B. Service Dominant Logic Perspective on Data Ecosystems - A Case Study based Morphology. 2020. Available from: https://www.researchgate.net/publication/341205004_Service_Dominant_Logic_Perspective_on_Data_Ecosystems_-_A_Case_Study_based_Morphology
Luthra S, Goel V, Kumar R. Exploration of Text Mining and Lexical Analysis Using a Novel Approach. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS). (pp. 187-192) IEEE. 2019.
Nandwani P, Verma R. A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining. 2021;11(1):81. Available from: https://doi.org/10.1007/s13278-021-00776-6
Dwaraki A, Freedman R, Zilberstein S, Wolf T. Using Natural Language Constructs and Concepts to Aid Network Management. In: 2019 International Conference on Computing, Networking and Communications (ICNC). (pp. 802-808) 2019.
Advaith V, Shivkumar A, Lakshmi BSS. Parts of Speech Tagging for Kannada and Hindi Languages using ML and DL models. 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). 2022. Available from: https://doi.org/10.1109/CONECCT55679.2022.9865745
Luca GD, Chen Y. Semantic Analysis of Concurrent Computing in Decentralized IoT and Robotics Applications. In: 2019 IEEE 14th International Symposium on Autonomous Decentralized System (ISADS). (pp. 1-8) IEEE. 2019.
Janda HK, Pawar A, Du S, Mago V. Syntactic, Semantic and Sentiment Analysis: The Joint Effect on Automated Essay Evaluation. IEEE Access. 2019;7:108486–108503. Available from: https://doi.org/10.1109/ACCESS.2019.2933354
Liu M. Discourse Analysis Model for Literary Works Appreciation and Evaluation by Regressive Analysis. In: 2020 International Conference on Artificial Intelligence and Education (ICAIE). (pp. 503-505) IEEE. 2020.
Matyakubovna KM, Eshimovna QN. The Role Of Text Pragmatics In The Study Of Computer Linguistics. In: 2021 International Conference on Information Science and Communications Technologies (ICISCT). (pp. 1-3) IEEE. 2021.

Copyright

© 2023 Rudrappa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)