Instant, Highly Accurate Speech-to-Text Transcripts
Capture every spoken word. Vistara AI transcribes voice conversations in real time, making call audio searchable and auditable.
Updated
Hinglish Speech Recognition
Fine-tuned models for bilingual Indian code-switching speech.
Custom Vocabulary & Product Names
Teach the model your model names, scheme names and industry terms so they are transcribed correctly instead of phonetically approximated.
Noise-Robust Recognition
Tuned for the conditions Indian calls actually happen in — traffic, shop floors, poor handsets and patchy signal.
Speaker-Separated Output
Dual-channel audio produces a clean per-speaker transcript, which makes talk-time and interruption analysis possible.
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Transcription quality is the invisible constraint on everything else a voice agent does. If the speech recognition mishears a number, the confirmation is wrong. If it drops the English word in a Hindi sentence, the intent classification fails. If it cannot handle a shop-floor background, the whole call degrades. Every downstream capability inherits the accuracy of this one step.
That is why Indian speech recognition is not a localisation feature to be added later. Models trained predominantly on American English handle Indian accents, Hinglish code-switching and Indian names poorly enough to break real conversations — and the failure is quiet, producing confident wrong answers rather than obvious errors.
Where Speech Recognition Fails on Indian Calls
Knowing the specific failure modes lets you design around them. These are the ones that matter most in practice, and each has a straightforward mitigation.
- Numbers and digits — Phone numbers, amounts and dates are the highest-consequence values and among the hardest to get right. Always read them back to the customer for confirmation.
- Proper nouns — Indian names, localities and product names are frequently approximated by models trained elsewhere. Custom vocabulary is the fix, and it is worth doing before launch.
- Code-switching — A Hindi sentence with English nouns breaks single-language models. This is the default way a large share of Indian customers speak, not an edge case.
- Background noise — Traffic, other conversations and television are normal conditions on Indian mobile calls, not exceptions to be excluded from testing.
- Fast or overlapping speech — When the customer talks over the agent, the audio overlaps. Good barge-in handling reduces this considerably.
- Poor line quality — Patchy signal produces dropped syllables. The correct behaviour is confirming rather than guessing, which has to be instructed explicitly.
The single highest-value habit: read back anything consequential. A confirmed wrong number is a wasted follow-up; a confirmed wrong appointment date is a customer who turns up on the wrong day.
Getting the Most From Your Transcript Archive
Transcripts are usually treated as a compliance artefact and filed. Used properly they are the fastest feedback loop available for improving both your agent and your marketing.
- Search for your own claims — Find every call where a specific feature or price was discussed and check what was actually said. This routinely surfaces gaps between your marketing and your reality.
- Mine unanswered questions — Every question the agent could not answer is a knowledge-base gap. Keep a running list and work through it weekly.
- Track language mix — What proportion of your calls are actually in Hindi or a regional language. Most companies significantly underestimate this, and it changes staffing and script decisions.
- Build a custom vocabulary from misses — Transcription errors cluster around a small set of terms. Fixing the top twenty produces most of the available accuracy gain.
- Spot-check after every change — Read fifty transcripts after any prompt or model change. Regressions are easy to introduce and hard to notice from aggregate metrics.