Speech-to-Text (STT)
The first stage of a voice agent pipeline that transcribes user audio into text in real time using deep neural networks.
Last updated: April 26, 2026
What Is Speech-to-Text (STT)?
Speech-to-text (also called automatic speech recognition or ASR) converts incoming audio into text the LLM can consume. Production STT for voice agents needs three things: real-time streaming (transcripts arrive token-by-token, not after the full utterance), accurate end-of-utterance detection, and low word error rate across accents and noisy phone-line audio. Top providers in 2026 are Deepgram (Nova-3), AssemblyAI, OpenAI Whisper (when self-hosted via faster-whisper), and Speechmatics. Phone-call audio is typically 8kHz mono PCM, which all major providers handle natively.
Two metrics matter most. First, latency to first interim transcript: production targets under 200ms. Second, word error rate (WER) on your specific user population: a Scottish accent or strong Indian English on a noisy phone line will perform very differently from clean studio audio in a benchmark. Always test STT on real recordings from your actual user base before committing. Keyterm boosting (telling the STT to pay extra attention to your product names, customer-specific vocabulary, or rare proper nouns) typically cuts the WER on critical terms by 30 to 50 percent.
When Should You Use Speech-to-Text (STT)?
Required for any voice agent built on the standard three-stage pipeline. Skip only if using a unified speech-to-speech model.
Related Terms
Building with Speech-to-Text (STT)?
I've shipped this pattern in real production systems. If you want a second pair of eyes on your architecture, that's what I do.