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Smart homes feature automatic fire/smoke detection, voice-operated assets and appliances, etc. More often, smart home appliances like lights, fans, etc. can be controlled through voice commands. Voice-operated devices like Alexa, Siri, and Google Assistant are not new in the current age concerning voice command execution. However, working with these supports requires a global connection with the internet that costs time and bandwidth. Controlling home appliances needs concise commands involving keywords on/off. Further, to operate the home appliances, bandwidth consumption for internet is not a wise idea. Through this paper, models based on temporal convolutional networks (TCN) and convolutional recurrent neural networks (CRNN) have been studied for keyword spotting (KWS) by training models with keywords pronounced in different accents. The performance of these models is compared, and their ability to detect unknown words is studied. Finally, how these models are suitable for building smart home assistants to control home utilities with minimum bandwidth consumption is discussed.

Small-Footprint Keyword Spotting for Controlling Smart Home Appliances Using TCN and CRNN Models