Voice recognition has not had an easy pathway to broad application. Early systems were incredibly buggy. Even the broadly used systems suffer from the occasional problem.
Case in point: My wife asked on app to phone me and was given a list of the nearest dog kennels. But by and large, the technology is getting there.
See below for some of the top trends companies and IT teams are seeing in the voice recognition market:
1. People First
Some voice recognition systems seem to only be there to deflect calls and choke down traffic to call center personnel.
While voice recognition systems are ever more sophisticated, they yet don’t always end up being that helpful. Although they now include artificial intelligence (AI) as part of their makeup, they may not necessarily be designed to actually help people.
“People are tired of AI cliches that technology is all-knowing and will solve every problem,” said Daniel Theobald, founder, Vecna Robotics.
“That’s why one of the biggest trends in 2022: will be putting people first.”
AI in robotics or voice recognition generally works well in controlled environments. However, when you add people, equipment, and other variables to the mix, humans generally reign supreme.
“Collaboration between man and machine requires thoughtfulness,” Theobald said. “The companies that put humans at the center of their operations and support new workflows will win big.”
2. Voice Adds to the Data Problem
A Domo report found that 1.7 MB of data was created every second for every person on earth. In recent times, voice recognition and chat have only added to the problem.
Microsoft Teams, for example, has more than 50,000 users connecting every minute. Zoom has hit more than 200,000 people meeting per minute at peak times. For every minute on any platform, it takes a great deal of storage capacity just to establish connectivity. Add in streaming video, chat, displaying presentations during meetings, voice, recordings of meetings, and automatically generating transcripts of meetings, and the amount of data boggles the mind.
Anyone enhancing voice features, therefore, or adding advanced voice recognition, had better pay close attention to data storage and system capacity if they wish their systems to scale efficiently.
3. Chatbots to Reduce Voice Traffic
Some enterprises are recognizing that voice prompt systems and automatic voice recognition systems may be adding to user frustration. One effort to reduce this is to introduce AI-powered chatbots to take some of the traffic away.
Grease Monkey International, for example, uses IBM Watson Franchising Virtual Assistant to help its business as one of the largest independent franchisers of automotive oil change centers in the U.S. The goal of the project was to increase the pace of lead generation as part of a rapid expansion initiative.
The system streamlines customer interactions by skimming off level one and two inquiries, instead of sending them directly to the franchising team. Low-level inquiries are dealt with using conversational AI. A pilot project performed so well that the company rolled Watson Assistant out to dozens of its centers.
4. AI Voice Must Learn Fast
Any AI system has to go through a learning period. Voice recognition is no different.
When it came to learning the ins and outs of competently addressing level one and two inquiries at Grease Monkey, Watson Assistant managed it in four days. It uses machine learning (ML) algorithms to train itself on user interactions and to find the ways to encourage users to provide complete contact information, without them hanging up or getting frustrated.
One good way to do this is to draw up a comprehensive set of frequently asked questions (FAQs) for initial training. This can be used as the basis for a natural language model that can understand customer inquiries with accuracy.
5. Teams Voice
Microsoft Teams has proven successful for chat and meetings. But more companies are now taking advantage of the platform’s full advanced voice and calling features. An integrated chat, video conferencing, and telephony experience is now available in the cloud.
A study by Forrester Research found that access to messaging, meeting, and calling within Teams can reduce downtime by 14.6%, and save around $650,000 over three years on average.
Such collaboration systems are continually adding new features. Some collaboration platforms are adding chatbots to help users. Chatbots, in fact, are one of the most popular of current voice recognition applications.
“These systems have grown in popularity for the ability to power applications that are proving to be helpful for businesses, from customer service chatbots to medical health lines,” said Rachel Roumeliotis, VP of AI and data content strategy, O’Reilly Media.
See more: The Artificial Intelligence (AI) Market