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Answer: Train a custom speech model using Azure Custom Speech to adapt to the specific background noise patterns.
Training a custom speech model using Azure Custom Speech allows the system to adapt to the specific background noise patterns commonly encountered in real-time transcription services. This customization improves the accuracy of the transcription, especially in varying noise conditions, making option C the correct choice.
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In a scenario where you need to implement speech-to-text functionality for a real-time transcription service, what steps would you take to ensure the accuracy of the transcription, especially in environments with varying levels of background noise? Consider the use of Azure services and any necessary customization.
A
Use Azure Cognitive Services Speech-to-Text with default settings.
B
Implement noise cancellation techniques before processing the audio with Azure Speech-to-Text.
C
Train a custom speech model using Azure Custom Speech to adapt to the specific background noise patterns.
D
Use a third-party speech-to-text API for better noise reduction.
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