Abstract

Trigger term, also referred to as a heat phrase or activation phrase, is a particular expression or wording that activates a voice-controlled system like a virtual aide when spoken. These trigger terms are widely employed in various devices, including mobile phones, intelligent domestic appliances, and automobiles. The precise identification of trigger terms is crucial to guarantee that the voice-controlled system only responds when the trigger term is uttered, disregarding other sounds. Numerous methods, such as regulation-based approaches and machine learning algorithms like support vector machines, concealed Markov models, and profound neural networks, have been proposed for identifying trigger terms. Elements like ambient noise, pronunciation variations, and accents may impact the accuracy of trigger term identification, and researchers are continuously exploring ways to enhance the resilience of these systems. As the utilization of voice-controlled systems continues to grow, the development of reliable and accurate methods for detecting trigger terms is increasingly imperative to ensure the seamless and efficient operation of these systems.

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