Discover the correct Azure AI capability to translate spoken French into English text for the Microsoft Azure AI Fundamentals AI-900 exam. Learn why “Speech-to-text translation” is the right answer.
Table of Contents
Question
You are working at a bank and need to build an application that captures spoken French and translates it into English text in a document.
Which of the following capabilities would you use?
A. Speech-to-text translation
B. Custom speech
C. Text-to-text translation
D. Entity Recognition
Answer
When building an application to capture spoken French and translate it into English text, the correct Azure AI capability to use is A. Speech-to-text translation.
Explanation
Speech-to-text translation is a feature of Azure’s Speech service that allows you to process spoken language input, convert it into text, and translate it into another language. Here’s why this is the right choice:
Core Functionality
The Speech service enables real-time transcription of audio in a source language (e.g., French) into text.
It then translates this transcribed text into a target language (e.g., English) using Azure Translator capabilities.
End-to-End Workflow
The process involves capturing spoken input through a microphone or audio file, converting it into text (speech-to-text), and then translating this text into another language (text translation).
Practical Use Case
This capability is ideal for multilingual applications like customer support tools, global conferencing systems, or document generation from speech inputs in different languages.
Why Not the Other Options?
B. Custom Speech:
Custom Speech is used to improve speech recognition accuracy for domain-specific vocabulary or unique audio conditions but does not inherently include translation capabilities.
C. Text-to-text translation:
This feature translates written text from one language to another but does not handle spoken input. It would require pre-existing text rather than audio.
D. Entity Recognition:
Entity Recognition is part of Natural Language Processing (NLP) and identifies specific entities (e.g., names, dates) within text but does not handle speech or translation tasks.
Conclusion
For applications requiring spoken language input in French to be translated into English text, Speech-to-text translation is the optimal choice. It integrates both speech recognition and language translation functionalities, making it a comprehensive solution for such tasks.
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