Bengaluru : Gnani.ai today released State of ASR Models in India 2026, the first industry study examining how ASR Models perform in real-world Indian voice environments.
The report’s findings reveal a widening gap between benchmark performance and real-world deployment conditions. Respondents reported a nearly 60% gap between expected and actual ASR accuracy on Indic language inputs. Developers also cited a production latency of more than 700ms, significantly above the threshold necessary for real-time voice agents.
While 68% of surveyed developers cited multilingual support as their primary selection criterion, they have explicitly mentioned the need for at least 8 Indic languages to cover production requirements. The report finds accuracy degradation of models during language switching and background noise as pertinent challenges in India.
“Benchmark accuracy means nothing if the model collapses on a real customer support call,” said Ganesh Gopalan. “Most speech AI benchmarks are measured on clean audio samples. India speaks on noisy phone calls, across multiple languages, often within the same sentence. That is the real deployment environment India’s Voice AI ecosystem is trying to solve for,” said Ganesh Gopalan, CEO and Co-founder, Gnani.ai.
Based on responses from over 800 Voice AI developers across 192 institutions, the report identifies major barriers preventing production-grade ASR deployment as well as potential opportunities in India.
The report also highlights rising enterprise demand for India-based hosting and compliance-ready ASR infrastructure driven by DPDP-related data residency concerns.