Researchers develop offline speech recognition that's 97% accurate

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. They describe their method in a paper published on the preprint server Arxiv.org It builds on work by Amazon's Alexa Machine Learning team, which earlier this year developed navigation, temperature control, and music playback algorithms that can be performed locally; Qualcomm, which in May claimed to have created on-device voice recognition models that are 95 percent accurate; Dublin, Ireland startup Voysis, which in September announced an offline WaveNet voice model for mobile devices; and Intel. "In this study, we explore a human-machine collaborative design strategy for building low-footprint [deep neural network] architectures for speech recognition through a marriage of human-driven principled network design prototyping and machine-driven design exploration," the researchers wrote.