Inclusive Voice-AI for Rural India: A Deep Learning Framework for Dialectal and Low-Resource Adaptation

Shubham Srivastava; Upendra Kumar Srivastava; Anjali Arora

doi:10.36948/ijfmr.2026.v08i01.68629

Inclusive Voice-AI for Rural India: A Deep Learning Framework for Dialectal and Low-Resource Adaptation

Author(s)	Mr. Shubham Srivastava, Dr. Upendra Kumar Srivastava, Ms. Anjali Arora
Country	India
Abstract	While voice-enabled multilingual AI chatbots are increasingly bridging service accessibility gaps in rural India, critical limitations remain in their capacity to handle dialectal diversity, cultural nuance, and infrastructural constraints. This research proposes a deep learning–based framework for developing inclusive, context-aware voice-AI systems optimized for low-resource rural environments. Leveraging transformer architectures and transfer learning, the study fine-tunes speech recognition and natural language understanding models on underrepresented Indian dialects using limited annotated corpora. To enhance cultural relevance and conversational naturalness, we integrate dialogue act recognition and emotion-aware response generation. Evaluation through participatory field trials with rural users will focus on usability, trust, and adoption metrics. Additionally, we explore model compression, edge computing deployment, and low-bandwidth optimization to ensure feasibility in connectivity-constrained regions. This work presents a scalable and ethically grounded blueprint for deploying socially responsive, deep learning–powered multilingual chatbots in underserved communities.
Keywords	Multilingual chatbot , rural AI, speech recognition, transformer models, transfer learning, low-resource NLP , edge deployment.
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-10
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.68629

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International Journal For Multidisciplinary Research

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Inclusive Voice-AI for Rural India: A Deep Learning Framework for Dialectal and Low-Resource Adaptation

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