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Über dieses Buch

Learn how to incorporate your own conversational interfaces into iOS applications. This book will help you work comfortably multiple frameworks, including Apple's Speech and SiriKit frameworks; Google's API.AI conversational interfaces platform; and Facebook’s

You'll explore the basics of natural language processing on iOS and see how to develop sentiment analysis with Apple's new Core ML framework. You'll also understand the primary challenges conversational interfaces face, and how to future proof your design.

With the introduction of SiriKit and the Speech framework, iOS developers now have huge opportunities to work with conversational interfaces in their apps. The latest advancements in natural language processing and machine learning allow for the development of complex conversational interfaces. This book incorporates all aspects of conversational interfaces on iOS—from voice transcription to natural language processing and entities extraction to text to speech commands.

What You'll Learn

Integrate intelligent voice interfaces into iOS applications

Use frameworks to enable voice reactive iOS application

Future proof your interface by understanding the expected future trends of voice recognition

Who This Book Is For

Primarily iOS developers, product and innovation managers, and UX experts. It will also be helpful to all developers/managers that want to provide conversational interfaces in their apps.



Chapter 1. Conversational Interfaces

People and computers speak different languages—people use words and sentences, while computers are more into ones and zeros. This gap in communication is filled with a mediator that knows how to translate all the information flowing between the two parts. These mediators are called graphical user interfaces (GUIs).
Martin Mitrevski

Chapter 2. Booking a Ride with SiriKit

At the WWDC conference in 2016, Apple announced SiriKit, which enables developers to provide functionality that can be executed directly from Siri, without opening the main application. This is another step to the idea of using new, innovative ways to interact with users via conversational interfaces, simplifying the whole user experience. Your app can now provide functionality to Siri directly from the lock screen and when the app is not even started. However, as is usually the case with Apple, there are some limitations. You can use SiriKit only for certain predefined domains.
Martin Mitrevski

Chapter 3. Creating Lists with SiriKit

Another interesting domain is Lists and Notes, which encompasses adding and removing items to and from a to-do list as well as adding notes. It’s a really handy domain, which you will explore in detail in this chapter. You will create an app that can add and remove items to and from a grocery list. Later in the chapter, you will see how you can write UI tests to verify whether your Siri implementation is correct.
Martin Mitrevski

Chapter 4. Speech, Synthesizers, and Dialogflow

At the same time SiriKit was announced, Apple also unveiled the Speech framework, the underlying voice recognition system that Siri uses. What does the Speech framework offer? It recognizes both live and prerecorded speech, creates transcriptions and alternative interpretations of the recognized text, and produces confidence levels of how accurate the transcription is. That sounds similar to what Siri does, so what’s the difference between SiriKit and the Speech framework?
Martin Mitrevski

Chapter 5. Getting Started with

As mentioned at the beginning of the book, all the big players are entering the exciting field of conversational interfaces. This means that Google’s Dialogflow is not the only option for analyzing and understanding the user’s spoken (or written) input. Facebook has its own product, called In this chapter, you will explore and compare it to Dialogflow.’s vision is to offer developers an open and extensible natural language platform that learns human language from every interaction. What’s interesting is that everything that’s learned is shared with all developers, which is quite useful because the platform is used by more than 120,000 developers.
Martin Mitrevski

Chapter 6. Natural Language Processing on iOS

Natural language processing (NLP) is a field in computer science that tries to analyze and understand the meaning of human language. It’s quite a challenging topic, since computers find it pretty hard to understand what humans are trying to say (although they are perfect for executing commands well known to them). By utilizing established techniques, NLP analyzes the text, enabling applicability in the real world, such as automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction, stemming, and more. NLP is commonly used for text mining, machine translation, and automated question answering.
Martin Mitrevski

Chapter 7. Sentiment Analysis with Core ML

What exactly is machine learning, a term that’s pretty popular at the moment? Machine learning allows computers to learn and make decisions without being explicitly programmed on how to do something. This is accomplished by algorithms that iteratively learn from the data provided. It’s a complex topic and an exciting field for researchers, data scientists, and academia. However, lately, it’s starting to be a must-know skill for good tech people in general. Regular users expect apps to be smarter, to learn from their previous decisions, and to give recommendations for their future actions. For example, when you are listening to songs in YouTube-generated playlists, you expect the next song to be tailored to your musical taste. You expect Google to filter out and not bother you with all the spam e-mails. You expect Siri to know exactly what you mean with your spoken phrases. Machine learning is all the magic behind the scenes that makes all this work. Since conversational interfaces would not work without this magic, you will explore it on iOS in this chapter.
Martin Mitrevski

Chapter 8. Conversational Interface Challenges

In the previous chapters, you learned about the current state of conversational interfaces on a technical level. In this chapter, you’ll see what challenges developers might face in this area and what you can expect in the future.
Martin Mitrevski


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