arXiv:2604.19151v2 Announce Type: replace Abstract: Existing Indic ASR benchmarks often use scripted, clean speech and leaderboard driven evaluation that encourages dataset specific overfitting. In addition, strict single reference WER penalizes natural spelling variation in Indian languages, including non standardized spellings of code-mixed English origin words. To address these limitations, we introduce Voice of India, a closed source benchmark built from unscripted telephonic conversations covering 15 major Indian languages across 139 regional clusters. The dataset contains 306230 utteranc
The increasing sophistication of AI models and the growing strategic importance of data, particularly domestic linguistic data, are driving nations to build their own AI infrastructure, especially for large, linguistically diverse markets like India.
This benchmark addresses critical limitations in existing Indic ASR, moving towards real-world, unscripted speech, which is crucial for developing robust and practical AI applications tailored to India's unique linguistic landscape.
The availability of a large-scale, real-world, unscripted telephonic conversation dataset in 15 Indian languages will significantly improve the accuracy and applicability of ASR for Indian languages, fostering domestic AI development.
- · Indian AI developers
- · Indian tech companies
- · Indian language speakers
- · AI-powered services in India
- · Companies relying on generic, non-Indic specific ASR
- · Existing Indic ASR benchmarks with scripted data
Improved speech recognition accuracy for Indian languages will enable more effective voice interfaces and AI applications localized for India.
This development could accelerate the creation of India-specific large language models and AI ecosystems, reducing reliance on foreign-built foundational models.
India might emerge as a leader in multilingual AI tailored for highly diverse linguistic environments, potentially influencing AI development in other diverse regions.
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