AccelWord

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Abstract
Voice control has emerged as a popular method for interacting with smart-devices such as smartphones, smartwatches etc. Popular voice control applications like Siri and Google Now are already used by a large number of smartphone and tablet users. A major challenge in designing a voice control application is that it requires continuous monitoring of user?s voice input through the microphone. Such applications utilize hotwords such as \"Okay Google\" or \"Hi Galaxy\" allowing them to distinguish user?s voice command and her other conversations. A voice control application has to continuously listen for hotwords which significantly increases the energy consumption of the smart-devices. To address this energy efficiency problem of voice control, we present AccelWord in this paper. AccelWord is based on the empirical evidence that accelerometer sensors found in today?s mobile devices are sensitive to user?s voice. We also demonstrate that the effect of user?s voice on accelerometer data is rich enough so that it can be used to detect the hotwords spoken by the user. To achieve the goal of low energy cost but high detection accuracy, we combat multiple challenges, e.g. how to extract unique signatures of user?s speaking hotwords only from accelerometer data and how to reduce the interference caused by user?s mobility. We finally implement AccelWord as a standalone application running on Android devices. Comprehensive tests show AccelWord has hotword detection accuracy of 85% in static scenarios and 80% in mobile scenarios. Compared to the microphone based hotword detection applications such as Google Now and Samsung S Voice, AccelWord is 2 times more energy efficient while achieving the accuracy of 98% and 92% in static and mobile scenarios respectively.

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