2 Oct 2022| ONPASSIVE
Role Of Artificial Intelligence Software In Voice Assistants
Chatbots are all the rage these days for both users and businesses, from marketing and sales to customer service. Chatbots can answer questions and engage in basic conversations with customers, website/app visitors, and social media users.
● Pre-programmed responses and call-to-action buttons are used. Here are two methods
● By using intelligent bots powered by artificial intelligence software
However, both chatbots have one thing in common. They respond to questions via text-based interfaces. In a nutshell, you’d communicate with a bot in the same way you’d share with a human.
Chatbots are extremely useful for automating customer service, marketing, and even sales tasks. But, in the future, will we be able to access chatbots with our fingertips, or will we need to type at all?
The future of chatbots may be speech-based, thanks to voice assistants.
Do the words “Alexa,” “Ok Google,” or “Hey Siri” ring a bell? These are called voice assistant, and tens of millions of people use them daily.
Bots that use artificial intelligence software, voice recognition, and natural language processing (NLP) to answer questions and hold conversations audibly are voice assistant.
While text-based interfaces necessitate machines processing text, analysing it, and mapping out a response, voice assistants do so audibly. To put it simply, instead of clicking on call-to-action buttons or typing out your question, you could speak to voice assistants.
However, voice assistants’ technology is quite complex and relatively new to text-based interfaces. Let’s take a closer look at voice assistant and their role in chatbot marketing to better understand how they work.
On the surface, we know that voice assistants answer questions and converse with users aloud rather than through text-based interfaces, but this is an oversimplification of how they work. The following are the various steps required for voice assistants to provide us with the answers we seek.
Passive listening devices include voice assistants such as Alexa, Cortana, and other consumer-facing bots. This means that the assistant is constantly scanning its surroundings for trigger words. When the trigger word is said loudly enough for the bot to hear, it will listen to the user’s query.
Other voice assistants, such as Siri or Google Assistant, can be passive listeners or tap/touch-activated. With recent concerns about data privacy, some users prefer more control over their devices.
The bot has been activated and is now ready to listen; however, how does it know what it is listening to? Voice recognition software, a subset of artificial intelligence software and deep learning, enables this.
For the machine to process, sound waves are converted into structured, more understandable data. Voice recognition will take everything from tone, pitch, volume, and speech precision into account.
Of course, this understates the difficulty of voice recognition, which is one of the most challenging problems in computer science today.
Before retrieving information, more complex nuances of human language must also be broken down. Context, user intent, slang, accents, and other loosely formal aspects of human language are all included.
Humans and machines are on entirely different wavelengths when it comes to language. While there are no hard and fast rules, devices require structure, detail, and process.
Natural language processing software is used by voice assistant to resolve any barriers to understanding.
After using voice recognition and NLP to process the user’s query, it’s time for the voice assistant to retrieve information related to the question. Voice assistants utilise various APIs and gain access to a knowledge base, which serves as a central repository to draw information.
The breadth of the knowledge base varies by device, but many mainstream voice assistants today are pretty detailed. An example of a knowledge base is as follows:
Over time, more information can be added to the knowledge base. This data is labelled, so machine learning knows precisely where to look for it. The larger and more organised the knowledge base, the fewer errors, and the chatbot will learn faster.
Now comes the final step, which is to output relevant information to the user. Many things have led to this point. Different tones, vibrations, and volumes are standardised for the machine with voice recognition. Natural language processing then assists the engine in comprehending what it has just heard. The information is then gathered from various sources. Hopefully, the result is a response that meets the user’s request.
To say there are many moving parts in the few seconds between asking a question and receiving an answer is an understatement. So, now that we know how voice assistants work let’s look at the applications for these complex bots.
Voice assistant have grown in popularity among consumers. More than 50 million Alexa devices are sold through Amazon. Most consumers use their devices to check the weather, who won last night’s game, the capital of Vermont, and other simple voice commands.
Only 2% of users make purchases through their voice assistants, and about 20% ask their assistants to check the status of online orders. Consumers’ lack of trust in voice assistants is primarily due to a graphical user interface (GUI). A graphical user interface (GUI) enables users to compare products, read reviews, and delve deeper into research.
Consumers and voice assistants go hand in hand, but we’ll soon see more businesses use voice assistants to automate day-to-day tasks. According to a recent survey of more than 600 senior decision-makers, 31% believe that voice technology benefits daily work.
For example, decision-makers rely on graphs, charts, and dashboards to break down KPIs and reports in business intelligence. The decision-maker can receive these reports audibly using a voice assistant, eliminating the need to shift priorities.
Another case is human resources (HR) and recruiting, which benefit from automation. Consider having a voice assistant break down various candidate profiles based on current employee baselines and models, as well as market data. This would eliminate lengthy processes, allowing the recruiter to focus solely on the candidate’s cultural fit, thereby streamlining the hiring process.
For the time being, voice assistants are better than humans at answering simple, non-business-related questions. On the other hand, Text-based chatbots take the cake for customer support, marketing, and sales tasks.
This is not to say that voice assistants aren’t the future; instead, more time is needed to map out business use cases. AI, NLP, and machine learning advancements will create new opportunities.
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