Struggling with Azure Speech Services’ regional transcription accuracy? Discover the exact 6-step process from creating custom speech projects to model deployment for AI-102 exam success. Master Microsoft’s recommended workflow to optimize region-specific vocabulary recognition in your certification scenario.
Table of Contents
Question
Xerigon Corporation has a subscription to Speech Services on Azure. They want to improve the audio transcription service integrated into an application. The current audio transcription service does not accurately transcribe region-specific vocabulary to the application.
How can you improve the audio transcription service?
Place the appropriate actions in the correct sequence.
Unordered Choices:
- Test recognition quality in Speech Studio of data.
- Upload audio files.
- Create a custom speech project.
- Deploy the model.
- Test and train the model.
- Choose speech to text model.
Answer
Correct Order:
- Create a custom speech project.
- Choose speech to text model.
- Upload audio files.
- Test recognition quality in Speech Studio of data.
- Test and train the model.
- Deploy the model.
Explanation
Xerigon needs to create a custom speech project with a standard speech to text model, upload audio data that contains industry-specific terms, test and train the custom model by checking the accuracy, and deploy the model to an endpoint that can be used by the application.
You will need to create a project and then choose a model. You should choose a Speech region if you train a custom model with audio data.
You will need to upload audio data to be used as test data to gauge the speech to text capabilities. You can click Upload data to upload audio files.
You would choose Audio as the type of file to upload.
Choose a zip file that contains the audio files that you want to upload.
You can test the recognition quality of the audio files by inspecting the recognition quality.
You would then choose test and train to train the model.
Speech Studio can analyze the speech recognition quality of the test data. To determine if more training is required, you can use the quantitative WER (word error rate) provided by the Speech service. When training a model, you should provide transcripts for the audio data.
When Xerigon is satisfied with the testing of the model, the model will be deployed to a custom endpoint. This endpoint will be used by the application. If you plan to use batch transcription, you do not have to deploy a custom endpoint.
Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.