Typically, deep learning approaches to voice recognition — systems that employ layers of neuron-mimicking mathematical functions to parse human speech — lean on powerful remote servers for bulk of processing. But researchers at the University of Waterloo and startup DarwinAI claim to have pioneered a strategy for designing speech recognition networks that not only achieves state-of-the-art accuracy, but which produces models robust enough to run on low-end smartphones.
