Despite being around for several decades in different forms, voice assistants have emerged into the technology scene in a much more advanced state in recent years, with major breakthroughs since the 2010s.
Fast forward a decade, voice recognition is now the celebrity of the tech industry, with experts foreseeing that the market will touch almost $30 billion by 2026 from $10.7 billion in 2020. According to a Google report, 27% of the global online population is using voice search on their mobile phones. With the growing popularity of this technology, it is undeniable that businesses should pay close attention to voice recognition software, such as voice assistants.
So how do voice assistants work and what are the benefits and challenges that come with them? How to overcome the challenges? What is a voice command and a skill command? How do you build your own solution? What voice assistant services are there and for what languages? What is the future of voice assistants? Look no further than our guide.
Maybe you’re already familiar with Siri by Apple, Alexa by Amazon, or Google Assistant. These are all examples of voice assistants. Voice assistants are digital assistants that rely on voice recognition technology to provide information or perform tasks commanded by humans, who simply have to interact verbally with these assistants.
The revolution of voice assistants emerged in the 2010s, together with the launching of smartphones and smart speakers, which are all interconnected. For example, in 2011, Apple’s Siri was launched together with the iPhone 4s; followed by the introduction of Amazon’s Alexa and Amazon Echo smart speaker in 2014; and in 2016, Google Assistant and Google Home smart speaker.
The voice assistants in the market all have their own strengths and weaknesses, but in general, they all carry out a plethora of tasks – typical examples include dialing and texting a contact, setting up appointments and reminders, answering questions, managing smart home devices, and playing music.
Fast forward to today, and voice assistants have become an increasingly common technology for human and machine interaction. They’ve expanded to a wider range of devices, such as smart TVs, watches, cars, and fridges, and with the help of artificial intelligence (AI), voice assistants make our everyday life easier.
Most of the voice assistants consist of four different components: Automatic Speech Recognition (ASR), Natural Language Processing (NLP), software assistants, and text-to-speech (TTS) technology.
When a user gives a command or asks a question to a voice assistant, ASR enables the machine to detect and translate their sentence from speech to text.
It is then NLP’s turn to help the system recognize the intent of the speech, including the meaning and the context.
After understanding the words and the intention, the software assistant processes different commands or intents and the machine can now respond.
Finally, TTS converts this response from written to spoken form, allowing the voice assistant to talk and to respond to what we are asking. All the steps take just a few seconds, and results are available almost immediately.
There are two types of voice assistants: conversational, and command based. Examples of the conversational type are, again, Siri or Alexa, where we can have conversations with the AI, which mimics human interaction. This is what we’ve been familiar with from movies and real life: we have a conversation with the AI system, and it instantly answers the questions we ask.
The other, command-based voice assistants don’t allow conversation. Instead, you just tell the ‘assistants’ what to do and they execute what you want!
21 languages: Arabic; Cantonese; Danish; Dutch; Finnish; English; French; German; Hebrew; Italian; Japanese; Korean; Malay; Mandarin; Norwegian; Portuguese (Brazil); Russian; Spanish; Swedish; Thai; Turkish
8 languages: English; French; German; Hindi; Italian; Japanese; Portuguese (Brazilian); Spanish
12 languages: Danish; Dutch; English; French; German; Hindi; Italian; Japanese; Korean; Norwegian; Spanish; Swedish
Motivated to implement a voice assistant to your business? We recommend the following steps to ensure success.
It’s important to define your goals right from the start of your voice assistant implementation. Your goals should be as clear and specific as possible. Try answering all these questions:
You planned for resources in the planning stage, and now it’s time to allocate them accordingly. Communicate the project to all stakeholders clearly, and ensure their roles are clear. We recommend an initial kick-off meeting to communicate roles, responsibilities, expectations and deadlines. Ensure all tasks are covered and understood, and that suitable technical resources are also allocated.
Do your research thoroughly and choose a voice assistant platform that is suitable for your business needs. Popular options include Apple Siri, Google Assistant, and Amazon Alexa. Consider elements like platform features, compatibility with your existing systems, and developer resources available.
Design a user-friendly and intuitive voice user interface that meets customer expectations and aligns with your brand. Take into account the voice tone, personality, and conversational flow of the assistant to ensure a positive user experience.
If necessary, develop custom skills for your voice assistant to execute certain tasks related to your business. For example, if you have an e-commerce store, you might want the assistant to provide product information, process orders, or answer customer inquiries.
Ensure seamless integration with your existing systems and processes. This can include linking the voice assistant to your CRM, e-commerce platform, inventory management system, or any other relevant business tools. APIs and integration tools provided by the voice assistant platform can assist with this step.
Thoroughly test the voice assistant's functionality, accuracy, and user experience. Gather feedback from testers and users to identify any areas for improvement. Iterate and refine the voice assistant based on user feedback and evolving business needs.
Read more: The ultimate guide to implementing Conversational AI in your business
Interesting fact: in 2020, 58% of consumers used voice search to discover local businesses, 46% among which do that daily. This proves that voice assistants benefit businesses significantly through helping customers during their buying journey. Let’s find out what these benefits are!
Voice assistants can make customer experiences quick, easy, convenient, and more personalized. They are smart enough to pick up from previous conversations and come up with personalized resolutions that are the most suitable for the customer.
Customers can speak to the installed voice assistant just as they would with a human customer representative. With the power of AI, voice assistants offer customers immediate answers and solve their inquiries, without any human intervention.
A quicker and smarter customer experience makes customers more likely to recommend to others and to come back as a returning customer.
A rapid and easy customer experience often leads to a successful conversation to sales. For example, quick and convenient product consultation provided by a voice bot can help an online customer to proceed to check-out smoothly, rather than keeping the products still in their cart.
Using a voice assistant helps companies save money on staffing expenses due to decreased demand for manpower. Therefore, the implementation of a voice assistant is an important decision to consider for your long-term business plan.
Time is money, and voice assistants help businesses to save that. Talking and listening to a voice assistant is of course quicker and more efficient than typing out questions and reading responses from a customer representative or even a chatbot.
A reduced handling time means improved work efficiency. With a voice assistant taking care of a large number of customer inquiries, your human employees can focus on more important tasks that really require their involvement.
Read more: How to develop a good chatbot
Not just customer inquiries, voice assistants can also automatically handle emails, reports, reminders, meetings, and many other mundane tasks instantaneously and efficiently.
Voice assistants are an example of digital technology that never stops working. They assist businesses with management of reports, data, and systems, creating a smooth day-to-day operation flow that is supervised constantly.
Now that smart technologies and devices are becoming a part of our every life, data privacy becomes a crucial concern. It is important to protect data privacy, especially in the case of voice assistants who are always in close contact with your customers. Keeping data and confidential information secure is one of the most critical concerns, especially in some specific sectors, such as banking and finance.
It is a priority for businesses to protect customers’ data and hence, they need to ensure that their voice assistants only collect and process essential data. Therefore, to prepare for the implementation of a voice assistant, businesses need to carefully check and comply with all privacy requirements and data protection applicable in their country, for example: the General Data Protection Regulation (GDPR).
See more: StageZero's checklist to ensuring privacy compliance globally
AI development is an extremely complex domain of technology that requires dealing with progressive algorithms and highly skilled expertise to run them.
Despite the outstanding advancement of AI services, it is not that easy to find skilled, experienced, and reliable field experts who specialize in setting up machine learning systems.
StageZero’s AI adoption in Europe 2022 report states that 56% of high-performing companies partner with data providers who collect real-world data. This factor of data acquirement distinguishes the high performers from the ones who are not so much ahead of the competition.
Costs and deployment can also be a challenge when it comes to building a voice assistant.
Adopting an ASR system requires a long-term vision that prepares how to manage resources, capital, and time involved in the implementation of the system.
Training language models for a voice assistant can take a significant amount of time and proficiency. Acquiring an ample amount of language resources or effectively utilizing the existing ones can be quite expensive. Overall, the process of manual development would put a significant strain on your finances.
Scarcity of multilingual knowledge is a big challenge for developing voice assistants.
Most existing NLP innovations in the world so far have a strict focus on English as a language. As there are over 7,100 languages in the world, it’s extremely challenging to develop NLP for all of them. Since English is not a universal language and hence not used fluently by all global users, 38% of users are reluctant to employ voice technology due to this AI’s language coverage issue.
If your business is attempting to implement a voice assistant in a specific location, the ASR will easily face failure if it’s not trained on the specific language models of that location or region. Even if it’s trained for the language, the ASR will face another roadblock, which is the task of distinguishing between different accents and dialects to achieve precise interpretation. For example, the optimum goal would be that a voice assistant understands even when a user gives a command using their accent and/or dialect, rather than only the common, more widely ‘accepted’ version which AI is more familiar with.
Read more: Multilingual Natural Language Processing: solutions to challenges
Businesses need to decide on what the capabilities should be for their voice assistant. Should it be able to recognize different speakers, or catered to just one person at a time? Having a clear decision from the start will help a lot with the implementation process later.
Constructing a minimum viable product using existing technologies can be a good strategy. Several cloud providers including Microsoft and Google have APIs available for text-to-speech and speech-to-text - that can then be linked to large language models (LLMs).
Fine-tuning and testing of data can be acquired from either actual users or from a data partner. It can save you a substantial amount of time to obtain data from a partner – we are talking about getting data off the shelf instantly compared to 6-12 months.
Read more: The importance of data in voice assistant development
Data security and privacy will be less of a concern if you work with a partner for data, they will handle the data privacy part when training and testing. However, later you will need to make sure data is stored and processed according to privacy regulations in the regions that your solution is available. For that you will likely need to consult a lawyer, however, you can shortcut that initially by following our checklist for privacy.
Running machine learning models has a monthly cost, which is made up of the cloud resources that you use to run it which is usually CPUs and GPUs for the different components. Different cloud providers have different pricing models for their algorithms usually listed on their websites.
You will notice that your solution will not work in all cases and languages, at that point you will need to determine whether the performance is good enough or whether you need more data as getting more data is the most reliable way of improving performance. You can see our list of suggested data partners here.
Read more: Collecting data for NLP models: what you need to be aware of and Real-world vs. synthetic data: Which to use in AI model training?
Currently, voice assistants are limited to specific functions. In the not-so-distant future, thanks to large language models, they will be able to assist with virtually any task. We see this as an inflection point when everyone will start using voice assistants and estimate that this is less than 12 months away. In other words, if you have had plans for developing your own solution or add-on to a voice assistant, now is really the time to start.
It is likely that voice assistants can become more integrated into numerous tools and environments. They are likely to be present in more types of appliances, such as wearable devices, making them even more accessible and prevalent in consumers’ daily lives.
Improvements in NLP will potentially advance the capabilities of future voice assistants. Very likely, they will be better at comprehending context as well as solving complex commands, and able to take part in more meaningful and natural conversations. This means voice assistants will be more valuable and helpful.
In the future, voice assistants will continue expanding their functions beyond current basic tasks. They will be upgraded to be able to handle more complex tasks, such as home automation, online shopping, health monitoring, and offering personalized recommendations across different domains.
We can expect voice assistants to become more adaptive and personalized to individual users. They will learn more from various factors such as user preferences, customer interactions, and behavior patterns to offer customized recommendations and experiences. This degree of personalization will stimulate deeper engagement that contributes to a smoother and more seamless user experience.
Expectedly, voice assistants will become more capable in numerous languages and can adapt themselves to different cultural settings. This function will initiate new markets and generate wider adoption globally, making them more accessible and inclusive to diverse communities.
If this piques your curiosity, then please get in touch to discuss your project requirements with us.
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