Pretty cool how they reduce the power consumption - when it first came out, "Hey Siri" required your device to be plugged in: > To avoid running the main proce

Pretty cool how they reduce the power consumption - when it first came out, "Hey Siri" required your device to be plugged in: > To avoid running the main processor all day just to listen for the trigger phrase, the iPhone’s Always On Processor (AOP) (a small, low-power auxiliary processor, that is, the embedded Motion Coprocessor) has access to the microphone signal (on 6S and later). We use a small proportion of the AOP’s limited processing power to run a detector with a small version of the acoustic model (DNN). When the score exceeds a threshold the motion coprocessor wakes up the main processor, which analyzes the signal using a larger DNN. > Apple Watch uses a single-pass “Hey Siri” detector with an acoustic model intermediate in size between those used for the first and second passes on other iOS devices. The “Hey Siri” detector runs only when the watch motion coprocessor detects a wrist raise gesture, which turns the screen on. At that point there is a lot for WatchOS to do—power up, prepare the screen, etc.—so the system allocates “Hey Siri” only a small proportion (~5%) of the rather limited compute budget. It is a challenge to start audio capture in time to catch the start of the trigger phrase, so we make allowances for possible truncation in the way that we initialize the detector. Another interesting nugget: > There are thousands of sound classes used by the main recognizer, but only about twenty are needed to account for the target phrase (including an initial silence), and one large class class for everything else. The training process attempts to produce DNN outputs approaching 1 for frames that are labelled with the relevant states and phones, based only on the local sound pattern. The training process adjusts the weights using standard back-propagation and stochastic gradient descent. We have used a variety of neural network training software toolkits, including Theano, Tensorflow, and Kaldi.