DRAG DROP
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You are building a transcription service for technical podcasts.
Testing reveals that the service fails to transcribe technical terms accurately.
You need to improve the accuracy of the service.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
1. Create Custom Speech project 2. Create speech-to-text model 3. Upload training datasets 4. Train model 5. Deploy model
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.
thanks for the provided references
It is true.
1. Create a Custom Voice Project 2. Create a speech-to-text model 3. Upload Training Datasets 4. Training Model 5. Implementation model
1. Create Custom Speech project 2. Create speech-to-text model 3. Upload training datasets 4. Train model 5. Deploy model
correct answer
According to the answer, then data should not be uploaded to train the model? Seriously? So how do you plan to train yourself if they're supposed to be technical words. I agree with zellck
1. Create Custom Speech project 2. Create speech-to-text model 3. Upload training datasets 4. Train model 5. Deploy model