What Is A Voice Assistant? Your Guide to the Talking Tech

"Ok Google, play The Tortured Poets Department" - and your favorite Taylor Swift album fills the room.

"Hey Alexa, where's my phone?" -- and a helpful chime guides you to its forgotten location.

"Hey Siri, tell me a joke" - and a burst of laughter cuts through the day's stress. This, my friend, is the power of voice assistants. 

Voice assistants are like having a personal AI butler at your beck and call. These are a subset of intelligent virtual assistants that take input from humans in the form of text, voice, and image to perform a task.

While the technology has been around for some time, the emergence of generative artificial intelligence tools like ChatGPT, has brought increased capability and interest to the field.

Let's learn how voice assistants work, the technology behind it, the popular voice assistants, and the future of this fascinating technology. 

How do voice assistants work?

While text-based interfaces like chatbot tool on a website require machines to process text, analyze it, and map out a response, voice assistants do this audibly. In simple terms, you could speak to voice assistants out loud instead of having to click on call-to-action buttons or type out your question.

The technology behind voice assistants, however, is quite complex and relatively new compared to text-based interfaces.

To get a better understanding of voice assistants, let’s look at how exactly they work.

1. Some bots use passive listening

Voice assistants like Alexa, Cortana, and other consumer-facing bots are considered passive listening devices. This essentially means the assistant is constantly monitoring its surroundings for trigger words. Once the trigger word is said loud enough for the bot to hear, it will begin listening to the user’s query. For example, "Hey Google" or "Ok Google" is the trigger word for Google Assistant. 

Voice assistants have options to be tap/touch-activated as some users prefer more control over their devices with recent concerns surrounding data privacy.

2. Voice recognition kicks in

The bot has been activated and now it’s ready to listen, but how exactly does it know what it’s listening to? This is made possible with voice recognition software, a subset of artificial intelligence and deep learning.

Sound waves are converted into structured, more understandable data for the machine to process. Everything from tone, pitch, volume, and the precision of speech will be factored in with voice recognition.

Of course, this is underplaying the complexity of voice recognition, as it’s one of the most challenging problems in computer science today.

3. Followed by natural language processing

More complex nuances of the human language also need to be broken down before information retrieval. This includes things like context, user intent, slang, accents, and other loosely formal aspects of the human language.

Humans and machines are on totally different wavelengths when it comes to language. While we have no rigid guidelines, machines require structure, detail, and process.

Voice assistants rely on natural language processing software to step in and resolve any barriers to understanding.

4. Information retrieval takes place

After processing the user’s query using voice recognition and NLP, it’s time for the voice assistant to retrieve information related to the question. Voice assistants do this by calling on various APIs and accessing something called a knowledge base, which acts as a central repository to draw information.

The depth of the knowledge base varies from one device to another, but many mainstream voice assistants today are quite fleshed out. 

More information can be added to the knowledge base over time. This information is tagged so machine learning knows exactly where to look for it. The larger and more organized the knowledge base, the fewer errors will occur and the faster the chatbot is able to learn.

5. Final output

Now, onto the final step, outputting relevant information for the user.

A lot has led up to this point. Different tones, vibrations, and volumes are standardized for the machine with voice recognition. NLP then assists the machine with understanding exactly what it just heard. Then, information is retrieved from a variety of sources. The end product is an answer that hopefully satisfies the user’s request.

It’d be an understatement to say there are a lot of moving parts in the few seconds between asking a question and receiving an answer. 

When to use voice assistants

Voice assistants have become quite popular amongst consumers. They use it via mobile apps on smartphones, smart speakers at home, and voice control in cars. Users use them to check the weather, who won last night’s game, what’s the capital of Vermont, get directions to a place, play music, and other simple voice commands.  You can use them 

• When your hands are full or you’re busy with other tasks, such as cooking, driving, or exercising.
• To assist individuals with disabilities, providing an easier way to interact with technology.
• For managing smart home devices, such as adjusting thermostats, controlling lights, or locking doors.
• To quickly get answers to questions, check the weather, or find information online without manually searching.
• For sending messages, making phone calls, or setting reminders and alarms without using your hands.
• To play music, audiobooks, or podcasts, and to control media playback.
• For managing schedules, creating to-do lists, setting reminders, and organizing daily tasks.

Voice assistants for business

While voice assistants have become commonplace for consumers, businesses are now embracing them too, fueled by the recent advancements in generative AI. This technology allows for more natural and dynamic interactions between humans and machines.

The rapid evolution of AI is propelling businesses to move beyond simple text-based chatbots that rely on pre-programmed responses. Voice assistants offer a more intuitive and efficient way to interact in the workplace.

Businesses are building different AI agents using large language models from companies like OpenAI, Google Cloud, and Amazon Web Services as they find use cases of generative AI-powered voice assistants everywhere. As humans set goals, these intelligent agents help them achieve them.

These tools can act as personal assistants and automate routine tasks such as answering frequently asked questions, providing hands-free note-taking during meetings, and controlling office equipment like lights and thermostats.

In customer service, voice assistants are increasingly deployed to handle inquiries, process orders, and provide support, reducing wait times and operational costs. For businesses in sectors such as retail, e-commerce, hospitality, and banking, this enhances the customer experience.

Are voice assistants the future?

For now, it’s evident that voice assistants are better at resolving simple, non-business-related questions for human users. But when it comes to customer support, marketing, and sales tasks, text-based chatbots ruled the rooster till now.

But, advancements in AI, NLP, and machine learning are opening up new opportunities.

One looming question is when users will be comfortable enough to make purchases through voice assistants. Without a GUI giving users more control, the answer may be “never.” This is why companies like Google have developed “portal” bots that provide the benefits of both GUI and voice assistance.

Is this the future? Only time will tell.  

Your digital assistant awaits

Voice assistants have come a long way from their initial introductions. They've transformed from simple novelty features to powerful tools. As technology continues to evolve, we can expect voice assistants to become even more intelligent, personalized, and integrated into our lives.

Why not experiment with a voice assistant today and see how it can make your life a little bit easier, more convenient, and perhaps even a little bit more fun?

Discover further insights into how AI chatbot tools close the divide between human interaction and technology.

This article was originally published in 2019. It has been updated with new information.