STT Provider Comparison

Deepgram Nova-2 is our top recommendation for live voice calls. It offers the lowest latency (under 300ms) while maintaining excellent accuracy. This fast response time is crucial for natural back-and-forth conversations.

All major providers achieve 90%+ word error rate (WER) on clean audio. Deepgram and Google tend to edge out others on phone-quality audio. Real accuracy depends on accent, background noise, and domain vocabulary.

Google Cloud STT has the broadest training on Indian English accents and regional languages. Deepgram also performs well with Indian English. For Hindi and other Indian languages, Google is the clear leader.

Lower latency means faster AI responses, making conversations feel natural. Above 500ms feels noticeably slow. Deepgram at ~250ms is ideal. For non-real-time use cases like call recording analysis, latency matters less.

Yes, our platform supports configuring different STT providers per agent. You might use Deepgram for English calls and Google for Hindi calls. You can also set up fallback providers for redundancy.

All providers handle background noise, but performance varies. Deepgram Nova-2 is particularly strong at noise suppression. For very noisy environments, consider dedicated noise cancellation before STT.

Pricing is similar across providers ($0.004-$0.006 per 15 seconds). Deepgram and Google are competitively priced. For high volumes, we can negotiate better rates. Our Cost Calculator shows exact per-minute costs.

Azure allows custom model training for specialized vocabulary (medical terms, product names, etc.). Google offers adaptation features. Deepgram uses its base model but handles most domains well out of the box.