Struggling with Azure AI-102 certification? Learn how to enhance speech recognition accuracy using custom solutions tailored for industry-specific terminology.”
Table of Contents
Question
Your organization, Nutex Corporation, is developing a voice-based customer service application that requires high accuracy in recognizing specific industry-related terminology and phrases. The standard speech recognition models provided by Azure AI Speech are not sufficiently accurate for your needs because they do not account for the specialized vocabulary used in your industry.
To address this, you plan to implement a custom speech solution to improve recognition accuracy.
You have created a new project and uploaded the test data. What should you do next?
A. Train the model.
B. Deploy the model.
C. Test the model quantitatively.
D. Test recognition quality.
Answer
D. Test recognition quality.
Explanation
The next step in the given scenario, after creating a new project and uploading the test data, is to test the recognition quality. This involves assessing how well the model recognizes the specific industry-related terminology and phrases that your industry requires. By evaluating the recognition quality, you can identify areas where the model needs further improvement before proceeding to the training phase. This step ensures that your test data is relevant and that the model is on track to meet your accuracy requirements. You can use Speech Studio to inspect and play back the uploaded audio.
Testing the model quantitatively is not the next step in the given scenario. This step comes after testing the recognition quality of the model. To determine if more training is required, you can use the quantitative word error rate (WER) provided by the Speech service.
Deploying the model is not the next step in the given scenario. Deploying the model is the final step in the custom speech solution process. It involves making the tested and trained model available for use in your application. However, deployment should only occur after the model has been thoroughly tested, both qualitatively and quantitatively, and refined as necessary.
Training the model is not the next step in the given scenario. Training the model is a crucial step in the custom speech solution process, but it should be done after you have tested the model quantitatively. When training the model, you should provide transcripts of the audio. Jumping directly to training without testing the recognition quality might lead to suboptimal results, requiring additional adjustments later.
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.