Microsoft Azure AI Engineer Associate AI-102 Practice Question
Your team is building a voice-activated kiosk that must remain offline until the customer says "Hey Woodgrove". You trained the wake word in Speech Studio and downloaded the generated Woodgrove.table file. Using the Azure Speech SDK for C#, which implementation meets the requirement to detect the wake word locally without sending audio to the cloud?
Instantiate a DialogServiceConnector in listen mode and set the Keyword to "Hey Woodgrove".
Instantiate an IntentRecognizer, assign your Conversational Language Understanding project ID, and set the KeywordModelName property to Woodgrove.table.
Instantiate a KeywordRecognizer with an AudioConfig and supply a KeywordRecognitionModel created from Woodgrove.table.
Instantiate a SpeechRecognizer with AutoDetectSourceLanguage and load Woodgrove.table as a grammar file.
Keyword spotting is performed completely on the client device. In the Speech SDK you create a KeywordRecognitionModel from the .table file that Speech Studio produces for the custom wake word, and then pass that model to a KeywordRecognizer (or a SpeechRecognizer started with StartKeywordRecognitionAsync) so that audio is evaluated locally until the keyword is detected. The other options would either rely on cloud processing (IntentRecognizer, DialogServiceConnector) or use APIs that do not accept a keyword model (.table files cannot be loaded as grammars or language-detection resources). Therefore, only the combination of KeywordRecognizer and KeywordRecognitionModel satisfies the offline wake-word requirement.
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Microsoft Azure AI Engineer Associate AI-102
Implement natural language processing solutions
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