Voice Content and Usability

We’ve been conversing for a long time. Whether to present information, perform transactions, or just to check in on one another, people have yammered aside, chattering and gesticulating, through spoken discussion for many generations. Only recently have we begun to write our conversations, and only recently have we outsourced them to the system, a system that exhibits a far greater affection for written communications than for the vernacular rigors of spoken speech.

Laptops have trouble because between spoken and written speech, talk is more primitive. Machines must wrestle with the chaos of human statement, including the squabbling and pauses, the gestures and body vocabulary, and the dialect variations that can impede even the most skillfully created human-computer conversation. In the human-to-human situation, spoken language also has the opportunity of face-to-face call, where we can easily interpret visual interpersonal cues.

In contrast, written language develops its own fossil record of dated terms and phrases as we commit to recording and keeping usages long after they are no longer relevant in spoken communication ( for example, the salutation” To whom it may concern” ). Because it tends to be more consistent, smooth, and proper, written word is necessarily far easier for devices to interpret and know.

Spoken language is not a pleasure in this regard. Besides the visual cues that mark conversations with emphasis and personal context, there are also linguistic cues and outspoken behaviors that mimic conversation in complex ways: how something is said, never what. Our spoken language conveys much more than the published word can actually contain, whether it’s rapid-fire, low-pitched, high-decibel, sarcastic, stiff, or groaning. But when it comes to tone interfaces—the devices we conduct spoken discussions with—we experience exciting difficulties as designers and content strategists.

Voice Compositions

We interact with voice interfaces for a variety of reasons, but according to Michael McTear, Zoraida Callejas, and David Griol in The Conversational Interface, those motivations by and large mirror the reasons we initiate conversations with other people, too ( ). We typically strike up a discussion in the following ways:

we need something done ( such as a transaction ),
we seek knowledge of anything ( some kind of data ), or
we are social people and want someone to talk to ( conversation for conversation’s pleasure ).

A second talk from beginning to end that achieves some goal for the consumer, starting with the words interface’s initial greeting and ending with the user exiting the interface, also fits into these three categories, which I refer to as interpersonal, technical, and prosocial. Note here that a conversation in our human sense—a chat between people that leads to some result and lasts an arbitrary length of time—could encompass multiple transactional, informational, and prosocial voice interactions in succession. In other words, a voice interaction is a conversation, but it may not always be one voice interaction.

Purely prosocial conversations are more gimmicky than captivating in most voice interfaces, because machines don’t yet have the capacity to really want to know how we’re doing and to do the sort of glad-handing humans crave. Additionally, there is ongoing debate about whether users actually prefer the type of organic human conversation that starts with a prosocial voice and progresses seamlessly into new ones. In fact, in Voice User Interface Design, Michael Cohen, James Giangola, and Jennifer Balogh recommend sticking to users ‘ expectations by mimicking how they interact with other voice interfaces rather than trying too hard to be human—potentially alienating them in the process ( ).

That leaves two different types of conversations we can have with one another that a voice interface can also have easily, such as one that focuses on a transactional voice interaction ( buying iced tea ) and another on learning something new ( discuss a musical ).

Transactional voice interactions

When you order a Hawaiian pizza with extra pineapple, you’re typically having a conversation and a voice interaction when you’re tapping buttons on a food delivery app. Even when we walk up to the counter and place an order, the conversation quickly pivots from an initial smattering of neighborly small talk to the real mission at hand: ordering a pizza ( generously topped with pineapple, as it should be ).

Alison: Hey, how are things going?
Burhan: Hi, welcome to Crust Deluxe! It’s chilly outside. How can I help you?
Alison, can I get a pineapple-onion pizza in Hawaii?
Burhan: Sure, what size?
Large, Alison.
Burhan: Anything else?
Alison: No thanks, that’s it.
Burhan: Something to drink?
Alison: I’ll have a bottle of Coke.
Burhan: You got it. That will cost$ 13.55 and take about fifteen minutes.

Each progressive disclosure in this transactional conversation reveals more and more of the desired outcome of the transaction: a service rendered or a product delivered. Transactional conversations exhibit a few key characteristics: they’re direct, to the point, and economical. They quickly dispense with pleasantries.

Informational voice interactions

Meanwhile, some conversations are primarily about obtaining information. Alison might only want to place an order at Crust Deluxe, but she might not want to leave without a pizza at all. She might be just as interested in whether they serve halal or kosher dishes, gluten-free options, or something else. Even though we have a prosocial mini-conversation once more at the beginning to establish politeness, we’re after much more.

Alison: Hey, how are things going?
Burhan: Hi, welcome to Crust Deluxe! It’s chilly outside. How can I help you?
Alison: Can I ask a few questions?
Burhan: Of course! Go right ahead.
Alison, do you have any menu items that are halal?
Burhan: Absolutely! On request, we can make any pie halal. We also have lots of vegetarian, ovo-lacto, and vegan options. Do you have any other dietary restrictions in mind?
Alison: What about gluten-free pizzas?
Burhan: For both our deep-dish and thin-crust pizzas, we can definitely make a gluten-free crust for you, without a problem. Anything else I can answer for you?
Alison: That’s it for now. Good to know. Thank you!
Burhan: Anytime, come back soon!

This dialogue is entirely different. Here, the goal is to get a certain set of facts. Informational conversations are research expeditions to gather data, news, or facts in search of the truth. Voice interactions that are informational might be more long-winded than transactional conversations by necessity. Responses are typically longer, more in-depth, and carefully communicated so that the customer is aware of the important lessons.

Voice Interfaces

Voice-based user interfaces use speech at the core to assist users in accomplishing their objectives. But simply because an interface has a voice component doesn’t mean that every user interaction with it is mediated through voice. We’re most concerned with pure voice interfaces, which are completely dependent on spoken conversation and lack any visual component, making them much more nuanced and challenging to deal with because multimodal voice interfaces can lean on visual components like screens as crutches.

Though voice interfaces have long been integral to the imagined future of humanity in science fiction, only recently have those lofty visions become fully realized in genuine voice interfaces.

IVR ( interactive voice response ) systems

Though written conversational interfaces have been fixtures of computing for many decades, voice interfaces first emerged in the early 1990s with text-to-speech ( TTS ) dictation programs that recited written text aloud, as well as speech-enabled in-car systems that gave directions to a user-provided address. We became familiar with the first real voice interfaces that could actually be spoken with the help of interactive voice response ( IVR ) systems, which were developed as an alternative to overburdened customer service representatives.

IVR systems allowed organizations to reduce their reliance on call centers but soon became notorious for their clunkiness. When you call an airline or hotel company, which is a common practice in the corporate world, these systems were primarily intended as metaphorical switchboards to direct customers to a real phone agent (” Say Reservations to book a flight or check an itinerary” ), which are more likely to happen when you call one. Despite their functional issues and users ‘ frustration with their inability to speak to an actual human right away, IVR systems proliferated in the early 1990s across a variety of industries (, PDF).

IVR systems have a reputation for having less scintillating conversations than we’re used to in real life ( or even in science fiction ), despite being extremely repetitive and monotonous.

Screen readers

The screen reader, a program that converts visual information into synthesized speech, was a development that accompanied the development of IVR systems. For Blind or visually impaired website users, it’s the predominant method of interacting with text, multimedia, or form elements. The most recent version of a voice-over-text format of content delivery is probably the one that is closest to it.

Among the first screen readers known by that moniker was the Screen Reader for the BBC Micro and NEEC Portable developed by the Research Centre for the Education of the Visually Handicapped (RCEVH) at the University of Birmingham in 1986 ( ). The first IBM Screen Reader for text-based computers was created by Jim Thatcher in the same year, which was later recreated for computers with graphical user interfaces ( GUIs ) ( ).

With the rapid growth of the web in the 1990s, the demand for accessible tools for websites exploded. Screen readers started facilitating quick interactions with web pages that ostensibly allow disabled users to traverse the page as an aural and temporal space rather than a visual and physical one with the introduction of semantic HTML and especially ARIA roles in 2008, enabling speedy interactions with the pages. In other words, screen readers for the web “provide mechanisms that translate visual design constructs—proximity, proportion, etc. in A List Apart, writes Aaron Gustafson, “into useful information.” ” At least they do when documents are authored thoughtfully” ( ).

There is a big draw for screen readers: they’re challenging to use and relentlessly verbose, despite being incredibly instructive for voice interface designers. The visual structures of websites and web navigation don’t translate well to screen readers, sometimes resulting in unwieldy pronouncements that name every manipulable HTML element and announce every formatting change. Working with web-based interfaces takes a cognitive toll for many screen reader users.

In Wired, accessibility advocate and voice engineer Chris Maury considers why the screen reader experience is ill-suited to users relying on voice:

I hated the way Screen Readers operated from the beginning. Why are they designed the way they are? It makes no sense to present information visually before converting it to audio only after that. All of the time and energy that goes into creating the perfect user experience for an app is wasted, or even worse, adversely impacting the experience for blind users. ( ) _ _ _

In many cases, well-designed voice interfaces can speed users to their destination better than long-winded screen reader monologues. After all, users of the visual interface have the advantage of freely scurrying around the viewport to find information, ignoring areas that are unimportant to them. Blind users, meanwhile, are obligated to listen to every utterance synthesized into speech and therefore prize brevity and efficiency. Users with disabilities who have long had no choice but to use clumsy screen readers might benefit from more streamlined user interfaces, especially more advanced voice assistants.

Voice assistants

Many of us immediately associate voice assistants with the subset of voice interfaces that are now commonplace in living rooms, smart homes, and offices with the film HAL from 2001: A Space Odyssey or Majel Barrett’s voice as the omniscient computer in Star Trek. Voice assistants are akin to personal concierges that can answer questions, schedule appointments, conduct searches, and perform other common day-to-day tasks. And because of their assistive potential, they are quickly receiving more attention from accessibility advocates.

Before the earliest IVR systems found success in the enterprise, Apple published a demonstration video in 1987 depicting the Knowledge Navigator, a voice assistant that could transcribe spoken words and recognize human speech to a great degree of accuracy. Then, in 2001, Tim Berners-Lee and others created their vision for a Semantic Web “agent” that would carry out routine tasks like” checking calendars, making appointments, and finding locations” (, behind paywall ). It wasn’t until 2011 that Apple’s Siri finally entered the picture, making voice assistants a tangible reality for consumers.

There is a significant variation in how programmable and customizable some voice assistants are compared to others due to the sheer number of voice assistants available today ( Fig 1 ). At one extreme, everything except vendor-provided features is locked down, for example, at the time of their release, the core functionality of Apple’s Siri and Microsoft’s Cortana couldn’t be extended beyond their existing capabilities. There are no other means by which developers can interact with Siri at a low level, aside from predefined categories of tasks like sending messages, hailing rideshares, making restaurant reservations, and other things, so even now it isn’t possible to program Siri to perform arbitrary functions.

At the opposite end of the spectrum, voice assistants like Amazon Alexa and Google Home offer a core foundation on which developers can build custom voice interfaces. For this reason, developers who feel stifled by the limitations of Siri and Cortana are increasingly using programmable voice assistants that are capable of customization and extensibility. Amazon offers the Alexa Skills Kit, a developer framework for building custom voice interfaces for Amazon Alexa, while Google Home offers the ability to program arbitrary Google Assistant skills. Users can choose from among the thousands of custom-built skills available today in the Google Assistant and Amazon Alexa ecosystems.

As businesses like Amazon, Apple, Microsoft, and Google continue to occupy their positions, they’re also selling and open-sourcing an unheard array of tools and frameworks for designers and developers that aim to make creating voice interfaces as simple as possible, even without code.

Often by necessity, voice assistants like Amazon Alexa tend to be monochannel—they’re tightly coupled to a device and can’t be accessed on a computer or smartphone instead. In contrast, many development platforms, such as Google’s Dialogflow, have omnichannel capabilities that allow users to create a single conversational interface that then manifests as a voice interface, textual chatbot, and IVR system upon deployment. I don’t prescribe any specific implementation approaches in this design-focused book, but in Chapter 4 we’ll get into some of the implications these variables might have on the way you build out your design artifacts.

Voice content

Simply put, voice content is content delivered through voice. Voice content must be free-flowing and organic, contextless and concise in order to preserve what makes human conversation so compelling in the first place. Everything written content is not.

Our world is replete with voice content in various forms: screen readers reciting website content, voice assistants rattling off a weather forecast, and automated phone hotline responses governed by IVR systems. We’re most concerned with the audiobook content being delivered as a requirement rather than an option.

For many of us, our first foray into informational voice interfaces will be to deliver content to users. One issue is that any content we already have isn’t in any way suitable for this new environment. So how do we make the content trapped on our websites more conversational? And how do we create fresh copy that works with voice-activated text?

Lately, we’ve begun slicing and dicing our content in unprecedented ways. Websites are, in many ways, massive vaults of what I call macrocontent: lengthy prose that can last for miles in a browser window while extending like microfilm viewers of newspaper archives. Back in 2002, well before the present-day ubiquity of voice assistants, technologist Anil Dash defined microcontent as permalinked pieces of content that stay legible regardless of environment, such as email or text messages:

An example of microcontent can be a day’s weather forecast [sic], the arrival and departure times for an airplane flight, an abstract from a lengthy publication, or a single instant message. ( ) _ _ _

I would update Dash’s definition of microcontent to include all instances of bite-sized content that transcends written communiqués. After all, today we encounter microcontent in interfaces where a small snippet of copy is displayed alone, unmoored from the browser, like a textbot confirmation of a restaurant reservation. The best way to learn how to stretch your content to the limits of its potential is through microcontent, which will inform both established and new delivery methods.

As microcontent, voice content is unique because it’s an example of how content is experienced in time rather than in space. We can instantly see when the next train is coming from a digital sign underground, but voice interfaces keep our attention occupied for so long that screen reader users are all too familiar.

Because microcontent is fundamentally made up of isolated blobs with no relation to the channels where they’ll eventually end up, we need to ensure that our microcontent truly performs well as voice content—and that means focusing on the two most important traits of robust voice content: voice content legibility and voice content discoverability.

Our voice content’s legibility and discoverability in general both depend on how it manifests in terms of perceived space and time.