Voice AI Latency Matters More Than Accuracy - Why On-Device WhisperKit Changed Everything
The biggest mistake in voice AI is treating it like a model choice. Most teams spend weeks comparing transcription accuracy between providers when the real killer is latency.
Cloud STT vs On-Device: The Real Difference
When we switched from cloud-based speech-to-text to on-device WhisperKit for our voice-controlled desktop agent, the accuracy improvement was marginal. What changed dramatically was the latency.
With cloud STT, there was always a perceptible delay - network round trip, queue time, processing, response. Users would speak, then wait, then speak again. The interaction felt like a turn-based game.
With on-device processing on Apple Silicon, the transcription happens almost instantly. Users stopped waiting and started talking naturally. That single change made the entire experience feel different - not incrementally better, but categorically different.
Interruption Handling Is the Hard Part
The latency fix revealed the next challenge - interruption handling. When someone starts talking mid-response, the agent needs to detect that, gracefully cut off the text-to-speech output, and resume without losing context.
This is harder than it sounds. You need to distinguish between background noise and intentional speech. You need to preserve what the agent was saying so it can reference it later. And you need to do all of this fast enough that the user does not notice the transition.
Why This Matters for Desktop Agents
Voice-controlled desktop agents live or die on responsiveness. A 500ms delay in a chat interface is fine. A 500ms delay when you are trying to control your computer with your voice makes the whole thing feel broken.
The lesson is simple - optimize for latency first, accuracy second. Users will tolerate imperfect transcription if the interaction feels instant. They will not tolerate perfect transcription if it takes two seconds to respond.
Fazm is an open source macOS AI agent. Open source on GitHub.