In a world obsessed with speed and convenience, AI speech recognition has created an era in which you can simply talk to your computer, smartphone, or home-hub and get the answers you’re looking for, without having to type anything on a keyboard.
Essentially, it’s all about teaching computers how to process audio data, instead of the standard written or “text-based” data that it’s used to. This technology is gradually being used to replace other input methods like clicking and typing, but it’s far from perfect. After all, human speech doesn’t necessarily follow the simple set of rules that a computer thrives on. Differences in lexicon, slang, and even dialect can all confuse machines and make it harder for them to do their job.
Yet, despite the roadblocks, more devices are constantly emerging in the marketplace that make the most of the speech recognition solution. In fact, in the near future, almost everyone in the western world will have access to speech recognition software either at home or at work.
Where Did Speech Recognition Come From?
Like with so many fields of scientific discovery in the current world, machine learning is responsible for many of the breakthroughs we’ve had with speech recognition. Google combined the latest cloud-based computing data to share valuable information with machine learning algorithms, helping them to learn from astronomical numbers of previous interactions.
While speech recognition has been around for a while now, it was Apple’s entry into the voice recognition market with “Siri” that officially grabbed the public’s imagination. Facilitated by decades of research, Siri became the very first AI-powered assistant to bring humanity and character to the somewhat complex world of speech recognition. Since, the marketplace has continued to grow at astronomical rates, with Microsoft Cortana, Amazon Alexa, and more.
The Evolving Nature of Speech Recognition
Though most people still imagine inefficient IVRs and confused bots when they think of speech recognition, developments in the AI world with natural language processing and natural language understanding have helped to ensure that today’s computers can understand more today than ever before. In fact, Microsoft’s speech recognition system can transcribe human rate with a minimal 5.1% error rate.
One of the key innovations helping speech recognition technology to evolve is the introduction of context-focused algorithms into the AI world. By introducing context into analytics, it’s easier for computers to guess at what people are saying during transcriptions. For instance, it might be hard to tell the difference between the phrases, “That’s not fair”, and “That’s not fur” at a glance. However, if a machine knows that the conversation is about fashion, then the second instance may be more likely.
Common Applications of AI Speech Recognition
Speech recognition technology is gaining popularity in many different areas. For instance, it’s common in device control, where users can simply say “Ok Google” to fire up a search on their smartphone. Additionally, in the business world, speech recognition is being used more frequently for voice transcription tasks where people need to go through large amounts of data to find important pieces of information for compliance or recording purposes.
Though there’s a long way to go before speech recognition technology is entirely accurate, we’re gradually moving towards a world where we’ll be able to talk with our computers.