Master the process of integrating custom keyword recognition into your application using Azure AI Speech SDK. Follow expert tips on downloading and applying the .table file for seamless voice activation.
Table of Contents
Question
Your organization, Xerigon Inc., is developing a voice-controlled smart device that will recognize specific custom keywords, such as “Wake up, SmartDevice” or “Begin operation,” to activate the device.
You are using the Azure AI Speech service and have decided to implement custom keyword recognition to ensure that the device responds accurately to the predefined phrases.
You have started implementing the solution in Python and configured your Azure Speech resource. You have also created a new model to define the custom keyword using Speech Studio.
What should you do next to implement custom keyword recognition in your application once the model has been generated?
A. Download the model, and use the .table file with the SDK.
B. Configure the model in the Azure portal without downloading.
C. Download the model, and use the .zip file with the SDK.
D. Export the model as a REST API, and use it with the SDK.
Answer
A. Download the model, and use the .table file with the SDK.
Explanation
To implement custom keyword recognition in your application once the model has been generated, you would download the model. The model is downloaded in the .zip file, which contains the file with a .table extension. You use the .table file with the SDK. This file contains the necessary configuration for the SDK to recognize and respond to the custom keywords in your application. It is then integrated with the Python SDK to enable keyword detection.
Downloading the model and using the .zip file with the SDK is partially correct. The .zip file is downloaded for the generated model. However, you cannot use the .zip file with the Python SDK. You have to extract the .table file from the .zip file to use with the Python SDK.
Exporting the model as a REST API and using it with the SDK is not the next step in the given scenario. Custom keyword models are not deployed as REST APIs. In the context of Azure AI Speech services, REST APIs provide a way for developers to interact with the Speech service programmatically, allowing them to perform various speech-related tasks without needing to install or directly integrate with an SDK.
Configuring the model in the Azure portal without downloading is not the next step in the given scenario. Simply configuring the model in the Azure portal is not sufficient for enabling custom keyword recognition in your application. You must download the model and use it with the SDK in your code. In the given scenario, you have already configured the model in the Azure portal. There is no need to configure it again.
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