Building a voice AI agent that actually works in the real world is harder than picking a model with the best benchmark score. The difference between a smooth conversation and a frustrating one often comes down to one layer that doesn’t get enough attention: the Speech-to-Text (STT) engine<\/strong>.<\/p>\n\n\n\n At Pranthora, we’ve built and deployed voice AI agents across industries \u2014 ecommerce, healthcare, real estate, and more. In the process, we’ve tested multiple STT providers in production-like conditions, and we’ve learned that latency, endpointing behavior, and noise handling matter just as much as raw transcription accuracy. Here’s what we found.<\/p>\n\n\n\n Most people building voice AI agents focus on the LLM layer or the voice synthesis model. But STT is where conversations can break down silently. A slow transcription delays the agent’s response. A poor end-of-speech detector causes the agent to interrupt or wait too long. Bad noise handling makes the system unreliable in real-world environments like call centers or home settings.<\/p>\n\n\n\n When evaluating STT providers, the key factors to consider are:<\/p>\n\n\n\n With that framework in mind, here’s our honest breakdown of five STT providers we tested.<\/p>\n\n\n\n If your voice agents are primarily English-language, AssemblyAI is the closest thing to a no-brainer choice<\/strong> right now.<\/p>\n\n\n\n Its real-time streaming API performs exceptionally well across the metrics that matter most for voice agents. End-of-speech detection is accurate, noise handling is solid, and latency is low enough to enable natural-feeling conversations. We didn’t encounter meaningful accuracy issues in our English-language tests \u2014 even in moderately noisy conditions.<\/p>\n\n\n\n The limitation to know:<\/strong> Streaming API language support is currently limited. If you’re building for multilingual use cases or Indian regional languages, AssemblyAI alone won’t cut it. But for English voice agents \u2014 sales, support, appointment booking, screening \u2014 it’s our first recommendation.<\/p>\n\n\n\n For teams building voice agents that need to handle Hindi, Gujarati, or other Indian regional languages<\/strong>, Sarvam’s Saaras models are worth serious consideration.<\/p>\n\n\n\n We tested Saaras with Gujarati and the transcription quality was genuinely good \u2014 better than most alternatives we’ve evaluated for regional Indian languages. The model is also fast, which is important for keeping agent latency in check.<\/p>\n\n\n\n The limitation to know:<\/strong> Connection stability is the main pain point. The streaming connection drops occasionally in production, which means you need to build reconnection logic into your infrastructure. It’s a solvable problem, but it adds engineering overhead that you should plan for.<\/p>\n\n\n\n Soniox offers real-time STT with built-in end-of-speech detection, and it holds up reasonably well for Indian regional languages.<\/p>\n\n\n\n In our testing, transcription accuracy was comparable to Sarvam for regional language tasks \u2014 which is a positive signal given how few providers perform well here. The automatic endpointing also worked reliably, which simplifies agent architecture.<\/p>\n\n\n\n The limitation to know:<\/strong> Transcription latency was slightly higher than Sarvam in our tests. For most use cases, this difference is manageable. But if you’re optimizing for sub-second response times in high-volume voice agents, it’s worth factoring in.<\/p>\n\n\n\n Tip: If you’re deciding between Sarvam and Soniox for Indian languages, test both with your specific language and use case \u2014 accuracy can vary meaningfully by dialect and domain.<\/em><\/p>\n\n\n\n Deepgram’s newer Flux model<\/strong> is a strong performer, particularly in end-of-turn detection \u2014 arguably the most important STT behavior for conversational voice agents. Getting this right means the agent knows exactly when to start processing and responding, which makes conversations feel more natural and less robotic.<\/p>\n\n\n\n Speed is solid, and the model performs well across most English scenarios.<\/p>\n\n\n\n The limitation to know:<\/strong> In noisy environments, we observed slightly lower transcription accuracy compared to AssemblyAI. If your use case involves calls from mobile devices or environments without controlled audio quality, this is worth testing carefully before committing.<\/p>\n\n\n\n Cartesia Ink is very fast. In terms of raw speed, it’s one of the quickest STT options we tested. It behaves similarly to a faster version of a Whisper-style model.<\/p>\n\n\n\n The limitation to know:<\/strong> Speed without accuracy isn’t enough for production voice agents. In our testing, transcription quality wasn’t at the level required for reliable conversations \u2014 particularly in cases where the agent needs to take action based on what it heard. We’d watch this one as the model matures, but it isn’t where we’d stake production workloads today.<\/p>\n\n\n\n There’s no single “best” STT provider \u2014 the right choice depends on your language requirements, latency budget, and the environments your users are calling from. Here’s how we approach it at Pranthora:<\/p>\n\n\n\n Building reliable voice agents isn’t just about picking the right model. It’s about understanding how each layer of your stack behaves under pressure \u2014 and designing for the gaps.<\/p>\n\n\n\n At Pranthora<\/a>, our voice AI platform uses a custom speech pipeline architecture<\/strong> that lets us swap and combine STT providers based on the language, use case, and performance requirements of each deployment. Rather than locking into a single STT, we route intelligently \u2014 using the best available model for the specific conditions.<\/p>\n\n\n\n This is part of why we’re able to maintain ~1\u20131.5 second end-to-end latency<\/strong> across our voice agents while supporting 10+ languages, including Indian regional languages like Gujarati, Hindi, and Tamil.<\/p>\n\n\n\n If you’re building voice agents at scale and want to understand how the STT layer fits into a production-ready architecture, explore Pranthora’s platform \u2192<\/a><\/p>\n\n\n\n The STT landscape for voice AI is evolving fast. What’s true today may shift as providers ship new models. But the evaluation framework \u2014 accuracy, latency, endpointing, language support, stability \u2014 stays constant.<\/p>\n\n\n\n If you’re building voice agents, test these providers with your actual audio data before committing. The differences show up in production in ways benchmarks don’t capture.<\/p>\n\n\n\n Curious what STT stacks others are running in production? Drop a comment below \u2014 we’re always interested in comparing notes.<\/em><\/p>\n\n\n\n
\n\n\n\nWhy STT Choice Matters More Than You Think<\/h2>\n\n\n\n
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\n\n\n\n1. AssemblyAI \u2014 Best-in-Class for English<\/h2>\n\n\n\n
\n\n\n\n2. Sarvam Saaras \u2014 Strong for Indian Languages<\/h2>\n\n\n\n
\n\n\n\n3. Soniox \u2014 Solid Accuracy, Slightly Higher Latency<\/h2>\n\n\n\n
\n\n\n\n4. Deepgram (Flux Model) \u2014 Strong Endpointing, Good Overall<\/h2>\n\n\n\n
\n\n\n\n5. Cartesia Ink \u2014 Fast but Not Yet Production-Ready<\/h2>\n\n\n\n
\n\n\n\nHow We Think About Choosing an STT Stack<\/h2>\n\n\n\n
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\n\n\n\nHow Pranthora Handles the STT Layer<\/h2>\n\n\n\n
\n\n\n\nFinal Thoughts<\/h2>\n\n\n\n
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