STT, TTS, and LLM: the three pillars of voice AI
Every voice AI system relies on three core components working together, speech to text to understand what's said, a large language model to decide how to respond, and text to speech to actually say it back.
Speech to text (STT)
This converts the caller's spoken audio into written text the system can process. Accuracy matters a lot here, since any mistranscription can cause the language model to respond to something the caller didn't actually say.
Large language model (LLM)
Given the transcribed text and context from the conversation so far, the language model decides what to say next, guided by instructions specific to the business and use case. This is the layer responsible for the actual content and intelligence of the response.
Text to speech (TTS)
The model's response, still just text at this point, gets converted into natural sounding spoken audio and played back to the caller. Voice quality and naturalness here directly affect how human the conversation feels overall.
Frequently asked
Not necessarily, many voice AI platforms combine best in class providers for each component rather than relying on a single vendor's full stack.
More in Technical
A voice agent system is typically built from a few connected layers, a telephony layer that handles the actual phone connection, a speech processing layer that converts audio to text and back, and a language model layer that decides what to say.
Voice AI architecture generally follows a pipeline, speech to text converts what's said into text, a language model decides on a response, and text to speech converts that response back into audio, all repeated for each turn of the conversation.
A voice agent API generally lets you send leads into a calling campaign programmatically, trigger and manage campaigns through code, and pull call data, transcripts, and outcomes back into your own systems on demand.
See these components working together live.
We'll walk through a technical demo in a 15 minute session.
