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  • Learning Microsoft Cognitive Services
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Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services

By : Leif Larsen Henning Larsen
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Learning Microsoft Cognitive Services

Learning Microsoft Cognitive Services

By: Leif Larsen Henning Larsen

Overview of this book

Take your app development to the next level with Learning Microsoft Cognitive Services. Using Leif's knowledge of each of the powerful APIs, you'll learn how to create smarter apps with more human-like capabilities. ? Discover what each API has to offer and learn how to add it to your app ? Study each AI using theory and practical examples ? Learn current API best practices
Table of Contents (14 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started with Microsoft Cognitive Services
1. Getting Started with Microsoft Cognitive Services
Cognitive Services in action for fun and life changing purposes
Setting up boilerplate code
Detecting faces with the Face API
Overview of what we are dealing with
Getting feedback on detected faces
Summary
2
2. Analyzing Images to Recognize a Face
2. Analyzing Images to Recognize a Face
Learning what an image is about using Computer Vision API
Diving deep into the Face API
Adding identification to our Smart-House application
Summary
3
3. Analyzing Videos
3. Analyzing Videos
Knowing your mood using the Emotion API
Diving into the Video API
Analyzing emotions in videos
Summary
4
4. Letting Applications Understand Commands
4. Letting Applications Understand Commands
Creating language-understanding models
Training a model
Executing operations based on commands
Summary
5
5. Speak with Your Application
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5. Speak with Your Application
Converting text to audio and vice versa
Knowing who is speaking
Verifying a person through speech
Customizing speech recognition
Summary
6
6. Understanding Text
6. Understanding Text
Setting up a common core
Correcting spelling errors
Natural Language Processing using the Web Language Model
Extracting information through textual analysis
Exploring text using linguistic analysis
Summary
7
7. Extending Knowledge Based on Context
7. Extending Knowledge Based on Context
Linking entities based on context
Providing personalized recommendations
Summary
8
8. Querying Structured Data in a Natural Way
8. Querying Structured Data in a Natural Way
Tapping into academic content using the Academic API
Creating the backend using the Knowledge Exploration Service
Answering FAQs using QnA Maker
Summary
9
9. Adding Specialized Searches
9. Adding Specialized Searches
Searching the Web from the Smart-House application
Getting the news
Searching for images and videos
Helping the user with auto suggestions
Search commonalities
Summary
10
10. Connecting the Pieces
10. Connecting the Pieces
Connecting the pieces
Real-life applications using Microsoft Cognitive Services
Where to go from here
Summary
Appendix A. LUIS Entities and Intents
LUIS pre-built intents
LUIS pre-built entities
Appendix B. Additional Information on Linguistic Analysis
Part-of-Speech Tags
Phrase types
Appendix C. License Information
Appendix C. License Information
Video Frame Analyzer
OpenCvSharp3
Newtonsoft.Json
NAudio

Chapter 5. Speak with Your Application

In the previous chapter, we learned to discover and understand the intent of a user, based on utterances. In this chapter, we will learn how to add audio capabilities to our applications. We will learn to convert text to speech and speech to text. We will learn how to identify the person speaking. Throughout this chapter, we will learn how you can utilize spoken audio to verify a person. Finally, we will touch briefly on how to customize speech recognition, to make it unique for your application's usage.

By the end of this chapter, we will have covered the following topics:

  • Converting spoken audio to text and text to spoken audio
  • Recognizing intent from spoken audio, utilizing LUIS
  • Verifying that the speaker is who they claim to be
  • Identifying the speaker
  • Tailoring the recognition API to recognize custom speaking styles and environments
End of Section 1
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