TinyML

Chapter 7. Wake-Word Detection: Building an Application

TinyML might be a new phenomenon, but its most widespread application is perhaps already at work in your home, in your car, or even in your pocket. Can you guess what it is?

The past few years have seen the rise of digital assistants. These products provide a voice user interface (UI) designed to give instant access to information without the need for a screen or keyboard. Between Google Assistant, Apple’s Siri, and Amazon Alexa, these digital assistants are nearly ubiquitous. Some variant is built into almost every mobile phone, from flagship models to voice-first devices designed for emerging markets. They’re also in smart speakers, computers, and vehicles.

In most cases, the heavy lifting of speech recognition, natural language processing, and generating responses to users’ queries is done in the cloud, on powerful servers running large ML models. When a user asks a question, it’s sent to the server as a stream of audio. The server figures out what it means, looks up any required information, and sends the appropriate response back.

But part of an assistants’ appeal is that they’re always on, ready to help you out. By saying “Hey Google,” or “Alexa,” you can wake up your assistant and tell it what you need without ever having to press a button. This means they must be listening for your voice 24/7, whether you’re sitting in your living room, driving down the freeway, or in the great outdoors with a phone in your hand. ...