If you own a smartphone, then chances are you have a capable, mature voice recognition system at your disposal. Smartphones and smart house systems such as Google Home and the Amazon Echo make use of voice recognition systems to handle user input naturally. Like image recognition, many of the most popular and successful voice recognition systems have been built through deep learning neural networks. As of 2016, China’s most dominant search engine, Baidu, holds the record in voice recognition accuracy at 96%, with Apple’s Siri coming in second place at 95%.
Now you just have to say the magic word, like “Siri” or “Okay Google” and you have a virtual assistant ready to help you with anything they’re programmed to do — get directions, find information, make a phone call, text your friend, schedule lunch, or make a purchase on your behalf. And they’re learning to do more and more every day.
Aside from a host of functions in your smartphone, you probably know that voice recognition has long been used in telecommunications in the form of automated telephone trees. But beyond that, you can find speech recognition software in healthcare where it’s used by patients to complete their medical forms or by doctors who transcribe spoken notes into digital documents. The ability to recognize a person’s voice is also widely used as an identification tool in business, legal, and consumer purchase environments. Teachers are also picking up on the technology to create online lesson plans that can be specially curated by them.
A stable technology, voice recognition has rapidly changed several industries and created new applications in others.
Whether it’s consumer-facing applications or internal systems, this software has a wide range of possible solutions to offer. Due to its convenience and familiarity in the market, voice recognition is a popular way to interact with users.