This approach is efficient when compared to existing implementations because the process of adding a new hotword in the existing systems requires enormous amounts of positive and negative training samples and the model needs to retrain for every hotword. This paper presents the design and implementation of a hotword detection engine based on one-shot learning which detects the hotword uttered by the user in real-time with just one or few training samples of the hotword. These hotwords are detected with lightweight real-time engines whose purpose is to detect the hotwords uttered by the user. are used widely across the globe for home automation, these require the use of special phrases also known as hotwords to wake it up and perform an action like "Hey Alexa!", "Ok Google!" and "Hey Siri!" etc. Voice assistants like Siri, Google Assistant, Alexa etc. The architecture proposed in this paper has achieved an accuracy of 95.40%. This approach is efficient when compared to existing implementations because the process of adding a new hotword to the existing systems requires enormous amounts of positive and negative training samples, and the model needs to retrain for every hotword, making the existing implementations inefficient in terms of computation and cost. The engine detects the hotword uttered by the user in real-time with just a few training samples of the hotword. This paper attempts to solve the same, by presenting the design and implementation of a lightweight, easy-to-implement hotword detection engine based on few-shot learning. However, existing engines require thousands of training samples or is closed source seeking a fee. These hotword detectors are lightweight real-time engines whose purpose is to detect the hotwords uttered by the user. They require the use of unique phrases, also known as hotwords, to wake them up and perform an action like “Hey Alexa!”, “Ok, Google!”, “Hey, Siri!”. Voice assistants like Siri, Google Assistant and Alexa are used widely across the globe for home automation.
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