How Voice Recognition is changing the tech job market

Machine learning provides the underlying technology that allows those systems to learn and improve over time

machine learning voice recognition


David Dewey is the Director of Research at Pindrop,

The shortage of technical talent in the United States has been a problem for more than a decade, and it’s especially acute for highly specialized positions. As technology changes and evolves, the need for different specialties can arise quickly, and the advent of voice as the next interface for computing has made audio engineers very sought-after commodities.

Traditionally, audio engineers have worked in fields such as music recording and TV production, but that’s changing quickly. Mobile phones, vehicles, smart TVs, and many other devices now have interactive voice capabilities and many of them use voice as the primary interface. Whereas remote controls or touch screens may have been the main interface just a couple of years ago, now voice has taken over that top spot in many cases. Voice interfaces allow for quick access to the features of smart devices, and developing those interfaces and the software that underlies them has become the sweet spot for audio engineers right now.

But finding the right people with the right skills has become an increasingly difficult task. This new breed of audio engineer needs not just signals processing skills, but also programming skills, and knowledge of machine learning, as well. The value of each of those individual skills depends on the demands of a given position, but the machine learning aspect has become more and more important recently. Like data science, the term machine learning is being thrown around in a lot of job descriptions right now, but it’s become especially valuable in organizations that need both programming and engineering skills.

One of the key advantages of voice-enabled systems is that they can learn from their users as time goes on. The systems can learn users’ vocal inflections, speech patterns, word choices, and other attributes and improve their accuracy continuously. This is true for voice-activated personal assistants on smartphones, entertainment systems in vehicles, and authentication systems that use voice as a primary factor. Machine learning provides the underlying technology that allows those systems to learn and improve over time, and so it has become a vital piece of many cutting-edge systems today.

But machine learning isn’t the only skill that organizations are looking for in top-level audio engineers. That experience is valuable, but it’s the mix of machine learning and programming skills that is really what many technology companies are after right now.

In a perfect world, top audio engineers have PhDs with a mix of both computer science and audio in their backgrounds and have the ability to adapt those skills to emerging technologies.

And it’s not just hard skills such as programming and signals processing that these teams are looking for. The nature of the problems that these engineers are working on dictates that they must be intuitive and have the ability to look at a problem from a variety of different perspectives and be comfortable using their instincts to solve them.

In that respect, the soft skill set is much like what many companies look for in top information security engineers: a high-level mix of technical skills and the intuition needed to solve hard problems.

As the market for voice-enabled systems evolves and the ways in which consumers use them changes and becomes more sophisticated, the mix of skills organizations are looking for on their audio engineering teams naturally will shift along with them. Engineers who have the ability to learn and adapt to those changes will end up being the most successful.

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