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How Speech Recognition Technology Really Works

Today, the world seems so very simple, surrounded as we are by so many smart devices such as smart phones, smart watches, and smart TVs, along with a veritable plethora of many other gadgets that we take for granted. So much so that we cannot even envisage life without them, and seem to have forgotten what it was like living in the not so distant past when such devices simply did not exist.

Few if any of us who take such technology for granted pause to reflect just how it has come about and the hard work that made it the success that it is today. While the idea of ‘simply’ being able to ‘speak’ to digital assistants in a manner they understand and can react productively to, seems to be entirely natural, it is due to the fact that it has been deliberately designed that way and has nothing to do what what lies ‘under the hood,’ so to speak.

In a nutshell, such simplicity is a bit of a misnomer since the underlying technology is anything but simple and it is a lasting tribute to the many individuals who have burned the proverbial ‘mid night oil’ to make this incredibly complicated technology seem as simple as it does today. If you’ve ever asked yourself, “how does speech recognition technology work,” then let us explain it below.

The core principles of Analysis, Filtration and subsequent Digitization

When we speak to our digital assistants, they ‘learn’ to analyze the sounds we make, courtesy an in-built filtration system that removes backgrounds static and other ambient noises. The software then proceeds to digitize it to a specific format (depending on the type of software used) so that it can actually “read” it and then commence analyzing it to determine precisely what you mean.

Since it does not have the intelligence of a human, it can only make educated guesses at best, as to what you want it to do. The accuracy of such guesses is directly responsible for speech recognition as we understand it today. This happens, courtesy certain exceedingly complex algorithms as well as previous inputs, through which it has ‘learnt’ to understand and recognize your voice and all its subtle nuances.

Here, it is comparatively easier for the person doing the programming to train their software to work with only one voice (their own, for example) but the challenges become more difficult when multiple users have to be taken into account along with their own dialects, modes of speech and even different languages, thereby making an already difficult task infinitely more so.

However, the industry has dedicated enormous resources to the engineering teams who are directly responsible for making the software work in a manner conducive to making our lives easier rather than more difficult, thanks to errors in transcription and understanding.

We are indeed, profoundly grateful to these pioneers and the speech recognition industry as a whole for the amazing products it has rolled out for us.