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Stream/Area:
-
Subject:
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Paper ID:
10.55041/IJSREM.RACCES052
Volume:
10 |
4 |
April 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology
CMR Institute of Technology, Bangalore
Information Technology
CMR Institute of Technology, Bangalore
The Kannada Dialect Detector and convertor is aninnovativeAI-drivenprojectdesignedtoidentifyandanalyse three key Kannada dialects—Bangalore, Dharwad, and Brahmin—by addressing the complexities of dialectal diversity through models for machine learning and a curated dataset. Unlike existing tools that treat Kannada as a single entity, this project considers dialect-specific pronunciation, vocabulary, and grammar, providing precise speech transcription and dialect classification. Beyond its technical utility, it bridges a significant gap in language processing, supporting applications such as educational content localization and voice-driven technologies while contributing to cultural preservation by explicitly distinguishing dialects. The effort not only advances regional language tools but also but also offers beneficial knowledge for researchers studying dialect evolution, fostering both technological innovation and appreciation of Kannada’s linguistic heritage.
KEYWORDS
The Kannada Dialect Detector and converter initiative has achieved notable progress in creating a tool capable of accurately identifying and analyzingsthediversedialectsof the Kannada language. Among its key achievements is the development of a powerful machine learning model thatutilizes advanced speech recognition and multiple techniques for processing natural language to differentiate between dialects. Throughoutthe project, Important lessons werediscoveredregarding aboutthecomplexitiesofdialectal variationsandTheimportofreliabledata collectiontoensure comprehensiverepresentation.Expanding thedata modelto includeunderrepresenteddialectsremainsavitalfuturestep, as it will enhance The versatility of the model and perform effectively across various contexts. Additionally, incorporating user feedback mechanisms can facilitate real- timesystemimprovementsandbetteralignthetoolwithuser needs. Deployment strategies are a critical focus for the project's nextphase. Transitioningtheprototype intoafullyfunctional webservicewillneedmeticulouspreparation,including,with considerations for scalability and user accessibility. Developingamobileapplicationversionwillfurtherbroaden user engagement, enabling on-the-go access to dialect detectioncapabilities.Theseeffortsaimtomakethetoolmore practical and widely available, supporting both individual usersandbroaderapplicationssuchaseducation,linguistics, and content localization. The project's impact extends beyond technological innovation to cultural preservation. By addressing linguistic nuances, the Kannada Dialect Detector not only enhances speech-driven applications like virtual assistants and transcription tools but additionally adds to the appreciation and preservation of Kannada's rich linguistic heritage. This project serves as a model for future research in dialect detection for other languages, fostering inclusivity in technology-driven solutions and bridging gaps in current voice recognition software for regional languages. Through the incorporation of cultural and technological advancements, the Kannada Dialect Detector and converter lays the groundwork for innovative solutions designed to meet the requirements of diverse linguistic communities
[1] Google, “Speech recognition technology and its applications,” 2022. [Online]. Available: https://cloud.google.com/speech-to-text/docs [2] IBM, “Watson Speech to Text,” 2021. [Online]. Available: https://www.ibm.com/cloud/watson-speech-to-text [3] S. Mittal and A. Kumar, “A review of natural language processing techniques for Kannada language,” International Journal of Computer Applications, vol. 975, p. 8887, 2020. [4] S. Bhargavi and K. Prasad, “Dialect detection in Indian languages:AcasestudyonKannada,”inProc.International ConferenceonCommunicationandInformation Processing, pp.150–157,IEEE,2019. [5] M. Vikas and P. Rao, “Machine learning techniques for dialect identification: A survey,” Journal of Computer Science and Technology, vol. 36, no. 2, pp. 345–369, 2021. [6] H.RanjithaandR.Shetty,“Featureextractiontechniques for dialect classification in Kannada language,” Journal of IndianLanguageTechnology,vol.2,no.1,pp.45–59,2020. [7] M. Karthik and S. Natarajan, “Deep learning approaches for speech recognition in Indian languages,” Journal of Software Engineering and Applications, vol. 15, no. 3, pp. 147–158, 2022. [8] AI Research Group, “Gemini AI: Capabilities and applications,” 2023. [Online]. Available: https://ai.google/research/gemini [9] IIIT Bangalore, “Natural Language Processing in Indian Languages:CurrentTrendsandChallenges,”2021.[Online]. Available:https://www.iiitb.ac.in/research/nlp [10] R.KumarandA.Rehman,“Statisticalmodelsfordialect detection: A comparative study,” Journal of Linguistics and Language Teaching, vol. 13, no. 4, pp. 321–339, 2022.
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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