Skip to content

shannonlowder.com

Menu
  • About
  • Biml Interrogator Demo
  • Latest Posts
Menu

Alexa Terminology and Documentation

Posted on January 18, 2018November 14, 2022 by slowder

n my last article, I introduced the hardware, the differences between the Alexa Voice Service (speech recognition and language understanding) and the Alexa Skills Kit (new and interesting abilities you can create for the Alexa ecosystem).  I also covered four current skills categories. Today let’s cover some key terms you’ll want to understand before beginning your first skill. Then I’ll share a list of resources that helped me in building Alexa Skills.

Terminology

Amazon Web Services — Amazon’s version of Microsoft Azure. Home of Alexa services and…

AWS Lambda — Amazon’s version of Azure Functions.  This service will run code you write in Node.js, Java, Python, or C# (.Net Core) without needing to provision Virtual Machines.  Your code runs as a response to an event.  In this case, the Alexa service will make a request to your Lambda program. The response is the output of the Lambda code (also known as a Lambda function).  You can set parameters for how much the service will scale up or out for your function.

As I mentioned in the previous article, Lambda didn’t support .Net core until recently. I’m also not a fan of their use of the term lambda function since that phrase also refers to shorthand functions used in LINQ expressions.  That being said, the ability to create a small program to respond to events is exactly the right tool for the job when it comes to implementing an Alexa skill.  Most skills require little horsepower, and anything more than a Lambda or Azure function would be overkill.

Interaction — a “conversation” between the user and Alexa. This can be as simple as a request from the user and a reply from Alexa, or a back and forth exchange lasting several minutes.

Interaction model — the words users can use with a specific skill.  Interaction models are defined in JSON and are composed of Intents and utterances.

Intent — is the main idea behind the user’s request.  In my first demo skill, I wanted a verbal calculator.  This calculator supported the four primary functions: add, subtract, multiply and divide.  With each request I made, I had one of these four intents. I wanted the skill to perform one of these intents. When defining these intents I use self-explanatory intent names, like AdditionIntent, SubtractionIntent, etc.

Utterance — A collection of words a user will speak in order to trigger a specific intent.  In my calculator example, some of the utterances I had for my AdditionIntent were: “Add <x> and <y>”, “Please add <x> and <y>”, “What is <x> plus <y>?”.

Slot — In our utterances, we often need variables to provide interesting results.  Imagine if I had to define every utterance for addition.  That would take forever.  Slots, allow us to define variables within the utterance.  You have to define a data type for the slot, and those don’t always line up with what you’d expect.

In C# you would expect string, int, DateTime. In utterances, Amazon has defined a number of slot types, and you can even define your own custom slot types. The out of the box slot types look like AMAZON.DATE, AMAZON.TIME, AMAZON.NUMBER.  These can be tricky at first.  I would suggest looking at existing slot types before trying to define your own.

Voice User Interface (VUI) —  Using your voice to interact with a computer, rather than a monitor, keyboard, and mouse.  What started as a dream in Star Trek: The Next Generation is closer than ever before.  We’ve still got a way to go in improving the accuracy of transcribing voice to text, and we also have plenty of work on building more natural interaction models, but we’re closer than we’ve ever been before!

 

Documentation

When getting started with Alexa, you can’t go wrong with Amazon’s own documentation.  You can find out the facts there. You can even find links to example skills, most of which are hosted on GitHub!

Using c# in Lambda to write your custom skill code?  Start here.  There are also some git hub repositories that helped me:

  • https://github.com/Silvenga/Slight.Alexa
  • https://github.com/timheuer/Slight.Alexa
  • https://github.com/timheuer/alexa-skills-dotnet
  • https://github.com/timheuer/alexa-csharp-lambda-sample

With terminology and these references in hand, we’ll start talking through some of the infrastructure that you’ll interact with, and build in order for your skill to come to life.  In the meantime, if you have any questions, send them in. I’m here to help!

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recent Posts

  • A New File Interrogator
  • Using Generative AI in Data Engineering
  • Getting started with Microsoft Fabric
  • Docker-based Spark
  • Network Infrastructure Updates

Recent Comments

  1. slowder on Data Engineering for Databricks
  2. Alex Ott on Data Engineering for Databricks

Archives

  • July 2023
  • June 2023
  • March 2023
  • February 2023
  • January 2023
  • December 2022
  • November 2022
  • October 2022
  • October 2018
  • August 2018
  • May 2018
  • February 2018
  • January 2018
  • November 2017
  • October 2017
  • September 2017
  • August 2017
  • June 2017
  • March 2017
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • August 2013
  • July 2013
  • June 2013
  • February 2013
  • January 2013
  • August 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • March 2010
  • January 2010
  • December 2009
  • November 2009
  • October 2009
  • September 2009
  • August 2009
  • July 2009
  • June 2009
  • May 2009
  • April 2009
  • March 2009
  • February 2009
  • January 2009
  • December 2008
  • November 2008
  • October 2008
  • September 2008
  • August 2008
  • July 2008
  • June 2008
  • May 2008
  • April 2008
  • March 2008
  • February 2008
  • January 2008
  • November 2007
  • October 2007
  • September 2007
  • August 2007
  • July 2007
  • June 2007
  • May 2007
  • April 2007
  • March 2007
  • February 2007
  • January 2007
  • December 2006
  • November 2006
  • October 2006
  • September 2006
  • August 2006
  • July 2006
  • June 2006
  • May 2006
  • April 2006
  • March 2006
  • February 2006
  • January 2006
  • December 2005
  • November 2005
  • October 2005
  • September 2005
  • August 2005
  • July 2005
  • June 2005
  • May 2005
  • April 2005
  • March 2005
  • February 2005
  • January 2005
  • November 2004
  • September 2004
  • August 2004
  • July 2004
  • April 2004
  • March 2004
  • June 2002

Categories

  • Career Development
  • Data Engineering
  • Data Science
  • Infrastructure
  • Microsoft SQL
  • Modern Data Estate
  • Personal
  • Random Technology
  • uncategorized
© 2025 shannonlowder.com | Powered by Minimalist Blog WordPress Theme