Update README.md

README.md

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-This is a domain-specific, multilingual agricultural speech dataset with a primary focus on Hindi, Telugu, Oriya designed for speech-to-text and automatic speech recognition tasks, featuring human-annotated transcriptions and intended for benchmarking ASR model performance in real-world agricultural scenarios
+This is a domain-specific, multilingual agricultural speech dataset with a primary focus on Hindi, Telugu, and Odia, designed for speech-to-text and automatic speech recognition (ASR) tasks. It features human-annotated transcriptions and is intended for benchmarking ASR model performance in real-world agricultural scenarios.
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+This paper presents a comprehensive benchmark of 10 ASR models for agricultural advisory use across Hindi, Telugu, and Odia, using 10,934 real-world Farmer.Chat audio recordings with human-annotated transcripts. It introduces Agriculture Weighted Word Error Rate (AWWER) and LLM-based utility scoring to better evaluate domain-critical agricultural terminology beyond traditional WER, CER, and MER metrics.
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+Publishing Context:
+The research paper titled “Benchmarking Automatic Speech Recognition for Indian Languages in Agricultural Contexts” is published on arXiv (arXiv:2602.03868), and the accompanying agricultural ASR benchmark dataset is publicly released on Hugging Face to support reproducible research and community-driven development.
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+Paper link:
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+https://arxiv.org/abs/2602.03868
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+https://arxiv.org/pdf/2602.03868