Book Image

Voice User Interface Projects

By : Henry Lee
Book Image

Voice User Interface Projects

By: Henry Lee

Overview of this book

From touchscreen and mouse-click, we are moving to voice- and conversation-based user interfaces. By adopting Voice User Interfaces (VUIs), you can create a more compelling and engaging experience for your users. Voice User Interface Projects teaches you how to develop voice-enabled applications for desktop, mobile, and Internet of Things (IoT) devices. This book explains in detail VUI and its importance, basic design principles of VUI, fundamentals of conversation, and the different voice-enabled applications available in the market. You will learn how to build your first voice-enabled application by utilizing DialogFlow and Alexa’s natural language processing (NLP) platform. Once you are comfortable with building voice-enabled applications, you will understand how to dynamically process and respond to the questions by using NodeJS server deployed to the cloud. You will then move on to securing NodeJS RESTful API for DialogFlow and Alexa webhooks, creating unit tests and building voice-enabled podcasts for cars. Last but not the least you will discover advanced topics such as handling sessions, creating custom intents, and extending built-in intents in order to build conversational VUIs that will help engage the users. By the end of the book, you will have grasped a thorough knowledge of how to design and develop interactive VUIs.
Table of Contents (12 chapters)

Machine learning in Dialogflow

Processing human conversation into something that machines can understand is challenging. Even today, with all the technology that's available, natural language processing is still a big challenge. Google has been in the business of natural language processing since day one, through Google Search. Google Search is all about natural language processing, where keywords or phrases are converted into meaningful machine-understandable forms, and then the backend server returns search query results to the user. Similarly, Dialogflow takes care of all the complexities of machine learning behind the scenes to process natural language and even add artificial intelligence to train data so that machines can become smarter over time. For example, whenever a user speaks, their voice is transcribed and then processed by Dialogflow and then matched against...