A wakeword is the trigger phrase that takes a voice AI from passive listening to active conversation. Wakeword detection is what makes always-on voice AI possible without uploading every second of audio to the cloud. The detector runs continuously on a tiny neural net, typically a few hundred kilobytes, that is optimized for one task: deciding whether the most recent few seconds of audio contain the wake phrase. Everything else gets dropped immediately. Wakewords are the privacy and bandwidth foundation of always-on voice.
WHAT TO LOOK FOR
On-device inference
The wakeword model runs in the browser or on the device's CPU, never the cloud. Audio that does not contain the wakeword is discarded immediately and never transmitted. This is the privacy guarantee that makes always-on listening acceptable to users.
False accept and false reject rates
A wakeword detector is judged on two error rates: how often it triggers on non-wakeword audio (false accept) and how often it misses an actual wakeword (false reject). Production wakewords aim for one false accept per 24 hours and one false reject per 100 attempts.
Custom wakeword training
Off-the-shelf wakewords like 'Hey Siri' or 'Alexa' are trained on millions of utterances. Custom wakewords for individual products are typically trained on tens of thousands of crowd-sourced recordings, which is why custom wakewords often have higher error rates than the brand defaults.
TLDR:Lucy OS1 supports an optional wakeword for hands-free activation. The detector runs locally in the browser using a small neural net, so no audio leaves the device until the wakeword fires. Once it does, the audio session opens and the standard pipeline takes over. For users who prefer click-to-talk activation, the wakeword can be disabled. For users who want truly hands-free operation while cooking, driving, or working, the wakeword turns Lucy into an always-available presence without the privacy cost of streaming audio continuously.
The wakeword model runs in the browser or on the device's CPU, never the cloud. Audio that does not contain the wakeword is discarded immediately and never transmitted. This is the privacy guarantee that makes always-on listening acceptable to users.
A wakeword detector is judged on two error rates: how often it triggers on non-wakeword audio (false accept) and how often it misses an actual wakeword (false reject). Production wakewords aim for one false accept per 24 hours and one false reject per 100 attempts.
Off-the-shelf wakewords like 'Hey Siri' or 'Alexa' are trained on millions of utterances. Custom wakewords for individual products are typically trained on tens of thousands of crowd-sourced recordings, which is why custom wakewords often have higher error rates than the brand defaults.
Optional second stage that verifies the wakeword was spoken by the device owner, not a guest or a TV. Reduces false accepts to near-zero in shared environments at the cost of a brief enrollment step.
The time between the wakeword finishing and the audio session being ready to accept commands. Sub-300 millisecond wake latency feels seamless; longer than that and users either repeat the wakeword or start speaking before the session is ready.
Visible or audible confirmation that the wakeword fired and the audio session is open. This is both a usability cue and a privacy guarantee that the user knows when audio is being captured.
QUICK COMPARISON
| Capability | Lucy OS1 | Most AI tools |
|---|---|---|
| Memory across sessions | ✓ Permanent, never resets | ✗ Resets after every session |
| Voice quality | ✓ Lucy OS1 Natural Voice (best-in-class) | ✗ Basic STT, struggles with noise |
| Calendar awareness | ✓ Reads Google Calendar in real time | ✗ No calendar access |
| Available 24/7 | Always on, any device | Available but stateless each time |
| Gets personal over time | ✓ Builds your context continuously | ✗ Starts from zero every session |
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