![]() You can further personalize your keyword model by choosing the right pronunciations. With the Custom Keyword portal on Speech Studio, you can generate keyword recognition models that execute at the edge by specifying any word or short phrase. Detecting a keyword in the middle of a sentence or utterance isn't supported. ![]() The current system is designed to detect a keyword or phrase preceded by a short amount of silence. The goal is to maximize the correct accept rate while minimizing the false accept rate. The false accept rate is also known as the false positive rate. False accept rate: Measures the system's ability to filter out audio that isn't the keyword spoken by a user.The correct accept rate is also known as the true positive rate. Correct accept rate: Measures the system's ability to recognize the keyword when it's spoken by a user.The current system is designed with multiple stages that span the edge and cloud:Īccuracy of keyword recognition is measured via the following metrics: For all stages beyond the first, audio is only processed if the stage prior to it believed to have recognized the keyword of interest. ![]() To balance accuracy, latency, and computational complexity, keyword recognition is implemented as a multistage system. A keyword requirement acts as a gate that prevents unrelated user audio from crossing the local device to the cloud. Keyword recognition acts as a privacy boundary for the user. Generally, virtual assistants are always listening. For virtual assistant scenarios, a common resulting action is speech recognition of audio that follows the keyword. Upon recognition of the keyword, a scenario-specific action is carried out. For example, "Hey Cortana" is the keyword for the Cortana assistant. The most common use case of keyword recognition is voice activation of virtual assistants. It's also referred to as keyword spotting. Keyword recognition detects a word or short phrase within a stream of audio.
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