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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM.RACCES018

AI-Based Voice Assistant for Accessing Government Services in Rural Areas

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM.RACCES018

Volume:

10 |

4 |

April 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem.RACCES018

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.

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Authors

MNP

Mariya Nithiya P

Department of Computer Science and Engineering

Idhaya Engineering College for Women, Chinnasalem,Kallakurichi District,India.

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SC

Sandhiya C

Department of Computer Science and Engineering

Idhaya Engineering College for Women, Chinnasalem,Kallakurichi District,India.

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GK

Gandhimathi K

Department of Computer Science and Engineering

Idhaya Engineering College for Women, Chinnasalem,Kallakurichi District,India.

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SL

Sabi L

Department of Computer Science and Engineering

Idhaya Engineering College for Women, Chinnasalem,Kallakurichi District,India.

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EK

Ezhilarasi K

Department of Computer Science and Engineering

Idhaya Engineering College for Women, Chinnasalem,Kallakurichi District,India.

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Mentors

DM

Divya M

Department of Computer Science and Engineering

Idhaya Engineering College for Women, Chinnasalem,Kallakurichi District,India.

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Abstract

Access to government services remains a significant challenge for rural populations due to limited digital literacy, language barriers, and unfamiliarity with modern technology. Although many government schemes and services are available online, rural citizens often struggle to access and understand them through conventional web portals and mobile applications. To address this issue, this paper proposes an Artificial Intelligence (AI) based voice assistant that enables users to interact with government service platforms using natural speech. The system integrates speech recognition, natural language processing, and text-to-speech technologies to provide a simple and user-friendly interface. Users can ask questions about government schemes, eligibility criteria, and application procedures in their local language, and the system delivers relevant responses through audio output. The system architecture includes modules for speech-to-text conversion, language understanding, query processing, and information retrieval. Experimental evaluation indicates that the proposed system achieves high speech recognition accuracy and provides quick response time, making it suitable for real-time use in rural environments. This solution can help bridge the digital divide and improve accessibility to government services for rural communities.

KEYWORDS

Key Words: Artificial Intelligence Voice Assistant Speech Recognition Natural Language Processing Rural Technology E-Governance.

Cite This Article

Mariya Nithiya P, Sandhiya C, Gandhimathi K, Sabi L, Ezhilarasi K, Divya M, "AI-Based Voice Assistant for Accessing Government Services in Rural Areas," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-4, April 2026. https://doi.org/10.55041/ijsrem.RACCES018

Full Article/Conclusion

This paper presented an AI-based voice assistant designed to improve access to government services in rural areas. By utilizing speech recognition and NLP technologies, the system enables rural citizens to obtain information about public services through simple voice commands. The proposed solution addresses challenges related to digital literacy and language barriers, making e-governance services more accessible to rural populations. With further development and integration with government platforms, such systems have the potential to significantly improve digital inclusion and service delivery in rural communities.

References

1. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97. 2. Keselj, V. (2009). Book review: Speech and language processing by daniel jurafsky and james h. martin. Computational Linguistics, 35(3). 3. Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13(3), 55-75. 4. Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266. 5. Lopatovska, I., & Williams, H. (2018, March). Personification of the Amazon Alexa: BFF or a mindless companion. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (pp. 265-268). 6. Reis, J., Santo, P. E., & Melão, N. (2019). Artificial intelligence in government services: A systematic literature review. In World conference on information systems and technologies (pp. 241-252). Springer, Cham. 7. Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech recognition using deep neural networks: A systematic review. IEEE access, 7, 19143-19165. 8. Rao, T. R. (2004). ICT and e-Governance for Rural Development. Center for Electronic Governance, Indian Institute of Management, Ahmedabad, 28, 312-315. 9. Schuller, B., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., Müller, C., & Narayanan, S. (2010). The INTERSPEECH 2010 paralinguistic challenge. In Proc. INTERSPEECH 2010, Makuhari, Japan (pp. 2794-2797). 10. Schultz, T., & Waibel, A. (2001). Language-independent and language-adaptive acoustic modeling for speech recognition. Speech Communication, 35(1-2), 31-51.

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Publication Date

5/1/2026

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