Google vs Alexa: Battle of the Bots
summary
Claim: centralize app logic once and deploy to Alexa and Google Assistant, reducing duplication. Shows code using Alexa Skills Kit, Dialogflow (API.AI), AWS Lambda (recommended) and a .NET Web API endpoint, with remote debugging on Azure and browser testing. Demonstrates dynamic input via Alexa’s search query/slots and Dialogflow contexts, SSML for voice styling, Watson Tone Analyzer and headless CMS (Drupal, Firebase) for editable responses. Builds a working Jedi demo and a Star Trek LCARs example on GitHub that share context and session attributes and deploys successfully.
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chapters (27)
- 0:00Introduction and setup
- 1:36Why voice bots matter
- 3:42My journey into voice development
- 5:08How voice skills work
- 8:40Google Actions vs Alexa Skills
- 10:51Basic conversation flow
- 13:20Programming voice skills
- 15:29Templated approaches
- 17:12Dialogue Flow deep dive
- 18:56Philosophy of voice design
- 20:44Cooperative responses
- 23:34Conversational implicature
- 29:18Key voice development terms
- 31:26Invocation names and endpoints
- 34:19Debugging and testing
- 37:11Making a basic skill
- 40:02Dynamic user input
- 41:26Custom slots and wildcards
- 43:55Capturing user feedback
- 45:22Speech synthesis markup language
- 47:11Live demo: Jedi Master skill
- 49:18Updating responses via CMS
- 50:22Analyzing user tone
- 51:24Scripting vs machine learning
- 52:08Accessibility and fallbacks
- 53:52Additional resources
- 54:56Closing and Q&A
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