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Microsoft Azure AI Fundamentals AI-900 Exam Guide

You're reading from   Microsoft Azure AI Fundamentals AI-900 Exam Guide Gain proficiency in Azure AI and machine learning concepts and services to excel in the AI-900 exam

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835885666
Length 288 pages
Edition 1st Edition
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Authors (2):
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Steve Miles Steve Miles
Author Profile Icon Steve Miles
Steve Miles
Aaron Guilmette Aaron Guilmette
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Aaron Guilmette
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Identify Features of Common AI Workloads FREE CHAPTER
2. Chapter 1: Identify Features of Common AI Workloads 3. Chapter 2: Identify the Guiding Principles for Responsible AI 4. Part 2: Describe the Fundamental Principles of Machine Learning on Azure
5. Chapter 3: Identify Common Machine Learning Techniques 6. Chapter 4: Describe Core Machine Learning Concepts 7. Chapter 5: Describe Azure Machine Learning Capabilities 8. Part 3: Describe Features of Computer Vision Workloads on Azure
9. Chapter 6: Identify Common Types of Computer Vision Solutions 10. Chapter 7: Identify Azure Tools and Services for Computer Vision Tasks 11. Part 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure
12. Chapter 8: Identify Features of Common NLP Workload Scenarios 13. Chapter 9: Identify Azure Tools and Services for NLP Workloads 14. Part 5: Describe Features of Generative AI Workloads on Azure
15. Chapter 10: Identify Features of Generative AI Solutions 16. Chapter 11: Identify Capabilities of Azure OpenAI Service 17. Chapter 12: Accessing the Online Practice Resources 18. Index 19. Other Books You May Enjoy

Identify features and uses for speech recognition and synthesis

As we saw in the NLP scenarios section, speech recognition and synthesis are tasks that can be provided by NLP as part of the speech area of AI.

In the following sections, you will explore the AI capabilities of speech recognition and speech synthesis.

Speech recognition

Speech recognition is, simply put, STT; it uses the capabilities of AI to detect spoken input and output it as written text. It uses advances in areas such as DL techniques and the availability of large training datasets.

Speech recognition can provide the following uses:

  • Generating text output from users’ spoken input requests
  • Generating a text response to a user based on speech input
  • Generating audio file narration from a script for a video
  • Generating subtitles for an audience
  • Generating close captions for videos, live and recorded
  • Generating notes from dictation
  • Generating text transcripts of audio...
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