AI speech recognition is highly accurate on Indian accents when you use Indian-first models. Generic English-trained engines are where accuracy collapses.
AI speech recognition is highly accurate on Indian accents, but only when you use models trained on Indian audio. Generic English-first engines were trained overwhelmingly on Western speech, so they mis-transcribe Indian accents, stumble on code-mixed Hinglish, and fall apart on phone-quality audio, which is why teams conclude speech recognition does not work when the real problem is the wrong model. Indian-first models like Sarvam AI are trained on exactly this: the accents, the mid-sentence switching between Hindi and English, and noisy real-world calls. In the Hindi voice agent proof of concept we built for Battery Smart, that accuracy was what let it resolve support calls end to end. The lesson is that accuracy is a model-selection problem, not a fundamental limit of the technology, and it's the foundation of our Hindi AI voice agent development work.
Real-world audio is the true test. A model that scores well on clean studio speech can still fail on a 4G call from a moving vehicle with background noise; Indian-first models are trained for that reality. The same real-world-audio problem is why accent neutralization software has to be trained on live call conditions too.
Code-mixing breaks naive systems. Callers switch between Hindi and English within a single sentence, and only models built for it transcribe that reliably. That's the same code-mixing challenge behind building voice agents that speak Hindi and regional languages.
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