Amazon announced a slew of new connected devices today, and with those came some new Alexa features that users will be able to take advantage of. The most interesting, by far, are the new privacy features coming to the latest Alexa devices. Soon, US customers can choose to have all of their voice commands processed on their Alexa devices. That means these audio clips won't be sent to the cloud and they'll be automatically deleted after the Alexa device processes them. The online retailer has received much scrutiny over the years when it comes to the privacy implications of its smart speakers and displays. Back in 2019, Amazon added a toggle that let users opt-out of having their voice recordings and message transcripts "manually reviewed," and before that, the company added an option to delete your entire voice history with one command.

I've spent a lot of time over the last year or so with Google's AIY Projects Voice Kit, including some time investigating how well TensorFlow ran locally on the Raspberry Pi attempting to use models based around the initial data release of Google's Open Speech Recording to customise the offline "wake word" for my voice-controlled Magic Mirror. Back at the start of last year this was a hard thing to do, it was really pushing the Raspberry Pi to its limits. However as machine learning software, such as TensorFlow Lite and other tools, have matured we've seen models being run successfully on much more minimal hardware. With the privacy concerns raised by cloud connected voice devices, as well as the sometime inconvenient need for a network connection, it's inevitable that we'll start to see more offline devices. While we've seen a number of "wake word" engines--a piece of code and a trained network that monitors for the special word like "Alexa" or "OK Google" that activates your voice assistant --these, like pretty much all modern voice recognition engines, need training data and the availability of that sort of data has really held smaller players. Realistically most people won't be able to gather enough audio samples to train a network for a custom wake word.