Voice Keyword Guard
Hear the word. Alert or mute it — entirely on device.
Voice Keyword Guard is a Flutter app that runs continuous keyword spotting with TensorFlow Lite. Users can get an alert when a word is spoken, mute matched words in live audio (including call mode), and enroll a personal voice sample — without uploading audio to a server.
Keyword control that stays private
The app wires tflite_flutter with a BC-ResNet keyword model, continuous recording, alert playback, and mute paths. Shared preferences and local storage keep configuration on device; permissions are handled explicitly for mic access across platforms.
Cloud voice features create trust and latency problems
Parents, professionals, and privacy-sensitive users want keyword alerts or muting without shipping every utterance to a vendor. Cloud ASR is powerful but the wrong default when the job is simply: detect a small keyword set and react locally, fast.
- Users will not accept continuous cloud audio upload for a keyword beep
- Mute mode needs low-latency detection in live audio
- Enrollment should personalize without a training cluster
- Call-mode constraints make audio routing non-trivial on mobile
A focused on-device listening product
We shipped three clear modes — Word Alert, Word Mute, and Enrollment — on a Flutter shell with a TFLite keyword model, local labels, beep assets, and an audio pipeline using record/audioplayers/fftea utilities so detection stays on the phone.
What users can do
- Continuous on-device keyword listening with alert playback
- Word Mute — detect keywords and mute them in live audio
- Call-mode mute path for supported scenarios
- Enrollment with a personal voice sample
- Local model + labels shipped as app assets
- Explicit mic permission handling across platforms
Model, pipeline, then product modes
- 01
Model integration
Bundled BC-ResNet TFLite model and labels, wired through tflite_flutter for on-device inference.
- 02
Audio capture path
Continuous recording and signal utilities so keyword frames can be scored in near real time.
- 03
Alert & mute modes
Productized the two reaction paths — play an alert vs mute matched audio — including call-mode considerations.
- 04
Enrollment & polish
Local enrollment flow, preferences, and integration tests so the experience feels like a product, not a model demo.
Flutter meets on-device ML
Voice Keyword Guard is built natively on Android using a focused, modern stack chosen for performance, maintainability, and a clean user experience.
ML that has to feel instant
Latency vs battery
Continuous listening must score often enough to catch keywords without turning the phone into a heater — frame sizing and model choice matter.
Mute is harder than alert
Playing a beep is easy; surgically muting live audio (especially around calls) forces careful audio session handling.
Privacy as a product feature
Keeping audio on device is the selling point — we designed storage and inference so nothing needs a backend to work.
Why this project matters
Voice Keyword Guard shows LogicsBeat can ship privacy-first ML products: real on-device inference, a clear alert/mute UX, and Flutter packaging — not a notebook demo wrapped in a WebView.
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