You're mid-sentence, hunting for a word, and your phone fires off a half-formed search into the void. Or you pause for breath, the assistant cuts you off, mishears everything, and confidently books the wrong restaurant. You weren't done. It had absolutely no idea.
So how does a voice assistant decide you've finished speaking? The answer is less about understanding language and more about reading silence like a seismograph.
The Silence Timer That Runs Everything
The core mechanism is called an end-point detector, and at its simplest, it's a countdown. The moment your voice energy drops below a certain threshold, a timer starts. If you don't produce sound again within a set window (typically somewhere between 700 milliseconds and 1.5 seconds, depending on the system and context), the assistant treats that silence as a full stop and ships your audio off for processing.
That's the whole trick, at a low level. Not comprehension. A stopwatch.
But a flat silence timer creates obvious problems. Background noise, a TV, a fan, a subway, can mask real silence. A thoughtful pause mid-sentence looks identical to the pause after a completed thought. So modern systems layer in several additional signals.
First, acoustic energy. Every spoken phoneme has a characteristic energy curve. The word "the" is quieter than "stop." End-point detectors track root mean square energy across short frames, roughly 10 to 25 milliseconds each, and look for a sustained drop rather than a single blip. One quiet frame means nothing. Fifty consecutive quiet frames is a meaningful signal.
Second, pitch and intonation. In English, a declarative sentence tends to fall in pitch at the end. A question rises. A mid-sentence pause holds relatively flat. Prosody models trained on millions of utterances learn to read these curves. When pitch drops and energy collapses together, that's a strong combined signal that you've reached a terminal boundary. A rising intonation at the end of a pause, on the other hand, nudges the system to keep waiting.
Third, and most interesting: partial language model scoring. Even before your audio finishes, the speech recognition model is already predicting what you might have said so far. It checks whether the partial transcript forms a grammatically plausible complete sentence. "Set a timer for ten minutes" scores high for completeness. "Set a timer for" scores very low. If the partial transcript looks like a fragment, the system can extend the silence threshold slightly, buying you a few hundred extra milliseconds to finish your thought. You probably never notice. It's a quiet courtesy baked into the pipeline.
When Two People Buy the Same Speaker and Get Different Results
Take Priya and Marcus. Same smart speaker, same firmware. Priya uses hers in a quiet home office. She says "Play something relaxing," lands a clean falling intonation, pauses, and it responds immediately. Marcus uses his in a kitchen with an extractor fan running. Same phrase, same pause, but the fan noise keeps the acoustic energy just above the silence threshold. The timer resets. He waits. He starts to repeat himself. The assistant captures both attempts, scrambles them, and plays death metal.
Same device. Same words. Different noise floor.
The end-point detector is reading the room as much as reading the speaker. This is why voice assistants have historically performed worse in noisy environments, and not primarily because the speech recognition itself fails (though it does struggle). The endpoint decision misfires first. You never get clean audio to transcribe if the system can't agree on when the audio ends.
The Assumption That Trips Most People Up
Most people assume the assistant is waiting to understand them before responding. It isn't. The endpoint decision fires before any deep language understanding happens. The system decides you're done, then sends a fixed audio chunk to the transcription model, then sends the transcript to the language model. Sequential steps, not simultaneous ones.
This is why speaking more slowly and clearly doesn't always help as much as you'd expect. If your slow speech introduces longer pauses between words, you might actually trigger the endpoint detector mid-sentence more often, not less. The deliberate pause you think reads as thoughtful reads to the detector as finished.
Here's what I'd ask anyone who's ever yelled at a smart speaker: did you try silencing the room instead of raising your voice? Louder voice against a loud background is still a narrow signal-to-noise margin. Silence the fan for two seconds, say your thing, done. That single adjustment outperforms every careful enunciation trick people pass around online, and it works because it addresses the actual problem rather than a polite fiction about how these systems function.
The practical upshot is this: speak at a natural, continuous pace. Don't rush, but don't insert theatrical pauses for emphasis either.
The assistant isn't waiting for you to finish your thought. It's waiting for the room to go quiet enough that it can pretend you have.