Mind the “Uuh” – for Arduino Nano 33 BLE Sense

By Benedikt Groß, Maik Groß, Thibault Durand

Mind the “Uuh” is an experimental training device helping everyone to become a better public speaker. The cute little companion is constantly listening to the sound of your voice, aiming to make you aware of “uuh” fill words. These fillers are easy to avoid, but you have to start noticing them. Now every time you give a presentation and you say “uuh” – you will be aware :)

The prototype of Mind the “Uuh” was carefully designed as simple as possible: There is a bell, a volume knob which controls how hard the bell is hit or to turn it silent, a counter for your “uuh” stats and a reset button. The product design is deliberately making references to classic alarm clocks to convey the nature of Mind the “Uuh” intuitively.

For the detection of the “uuh”s the device runs a custom trained machine learning model, trained on 1500 samples of various durations from 300ms to 1 sec. This proof of concept model will notice distinctive “uuh” fillers but ignore very short utterances. All speech data is processed directly on device, nothing is sent to the cloud.

💻 Hardware pre-requisite (part list)

Part list:

• Microcontroller: Arduino Nano 33 BLE Sense
• Bell: we are using a standard bike bell for handle bars
• Servo: Tower Pro SG51R
• Display: standart seven segment display (sourced via 74HC595 shift register / drain via TPIC6B595)
• Poti: 10 kΩ
• Stripboard: 70x90 mm
• Housing: 3d printed PLA (print your own device)

📦 Software pre-requisite

1. Add the Edge Impulse library through the Arduino IDE via: Sketch > Include Library > Add .ZIP Library ... see .zip file ei-um-uh-ah-detector-...something.zip in this repo.
2. Upload the mind-the-uuh-arduino.ino onto your Arduino.
3. Done :)

💪 Training your own model

The "uuh" model was completely trained with Edge Impulse Studio on a free account. If you want to train your own hotword, we recommend following carefully the super great Responding to your voice video tutorial. You will have to prepare ca. 1500 samples of 1 sec duration as mono .wav files of 16000 hz for the training.

Acknowledgment

• Training of the model: Edge Impulse Studio
• Understanding how to train the model: Edge Impulse Tutorial Responding to your voice
• Web App: based on Edge Impulse example Demo-Shower-Timer