Indian Journal of Science and Technology
Year: 2023, Volume: 16, Issue: 21, Pages: 1572-1579
Naveenkumar T Rudrappa1*, 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
Email: [email protected]
Received Date:28 March 2023, Accepted Date:06 May 2023, Published Date:31 May 2023
Objectives: To develop a system that accepts cross-lingual spoken reviews consisting of two to four languages, translate to target language text for Indic languages namely Kannada, Hindi, Telugu and/or English termed as cross lingual speech identification and text translation system. Methods: Hybridization of software engineering models are used in natural languages for pre-processing such as noise removal and speech splitting to obtain phonemes. Combinatorial models namely Hidden-Markov-Model, Artificial Neural Networks, Deep Neural Networks and Convulutional Neural Networks were deployed for direct and indirect speech mapping. Trained corpus consisting of thousand phonemes in the form of wave files for each language considered is named as KHiTEShabdanjali. The basic parameters cosidered for training dataset are pause, pitch, sampling frequency, threshold etc. Findings: The research has resulted in the development of mono-lingual and multi-lingual speech identification, tool for processing of cross-lingual speech and language identification, mono-lingual, bi-lingual, tri-lingual and quad-lingual speech to monolingual text translation for the four languages. It is a generic approach and can be used for other regional languages of India by training the corpus with the selected language. Novelty: Cross-lingual speech identification and text translation system helps users in e-shopping by reducing the time incurred in making a decision to purchase a product having enough features at an economical price, e-tutoring, e-farming activities, digitizing, defence etc.
Keywords: Artificial Neural Networks; Convulutional Neural Networks; Deep Neural Networks; HiddenMarkovModel; Speech Processing
© 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)
Subscribe now for latest articles and news.