Exam AI-102 topic 3 question 42 discussion

https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/custom-speech-overview#how-does-it-work With Custom Speech, you can upload your own data, test and train a custom model, compare accuracy between models, and deploy a model to a custom endpoint. - Create a project and choose a model. Use a Speech resource that you create in the Azure portal. If you will train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. - Upload test data. Upload test data to evaluate the speech to text offering for your applications, tools, and products. - Train a model. Provide written transcripts and related text, along with the corresponding audio data. Testing a model before and after training is optional but recommended. - Deploy a model. Once you're satisfied with the test results, deploy the model to a custom endpoint. With the exception of batch transcription, you must deploy a custom endpoint to use a Custom Speech model.

model how to accurately recognize and transcribe technical terms. These datasets provide the raw data that the model learns from. Uploading training data comes after creating the model because you need to associate the data with a specific model. You cannot train a model without first creating it and then supplying the necessary training data. 4) Train the model: Training the model involves using the uploaded datasets to adjust and optimize the model's parameters. This process improves the model's accuracy in recognizing and transcribing the types of speech found in your training data, particularly the technical terms. Training must occur after you have both the model and the training data in place. The model needs the datasets to learn from, and you can't train a model without data.