Now Algorithms Are Deciding Whom To Hire, Based On Voice

Now Algorithms Are Deciding Whom To Hire, Based On Voice

March 23, 2015 4:40 PM ET

By Aarti Shahani

If you're trying out for a job in sales, the person who judges your pitch may not be a person — it could be a computer.

Job recruitment is the newest frontier in automated labor, where algorithms are choosing who's the right fit to sell fast food or handle angry cable customers, by sizing up the human candidates' voices.

Let's take a voice you know: Al Pacino. Think back to how he sounds in The Godfather, Devil's Advocate, Scarface or this recent interview on Charlie Rose.

The actor speaks with different accents, different emotions, different ages — and his range is stunning. But in every version, Pacino's voice has a biological, inescapable fact.

"His tone of voice generates engagement, emotional engagement with audiences," says Luis Salazar, CEO of Jobaline. "It doesn't matter if you're screaming or not. That voice is engaging for the average American."

Years and years of scientific studies and focus groups have dissected the human voice and categorized the key emotions of the person speaking.

Jobaline has taken that research and fed it into algorithms that interpret how a voice makes others feel and then cross-checks its judgment with real human listeners. It's a departure from other data science. With facial recognition, for example, algorithms sift through your smile, your brow, to decide your mood.

With the technology to conduct more nuanced tests, some companies say they can provide more useful detail about how people think in dynamic situations.

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In the hiring process, there are things employers aren't permitted to ask, like whether you plan to have kids. Some employers turn to social media to learn more about job candidates.

"We're not analyzing how the speaker feels," Salazar says. "That's irrelevant."

Regardless of whether you're happy, sad or cracking jokes, your voice has a hidden, complicated architecture with an intrinsic signature — much like a fingerprint. And through trial and error, the algorithms can get better at predicting how things like energy and fundamental frequency impact others — be they people watching a movie, or cancer patients calling a help line.

Through machine learning and multiple feedback loops, it keeps answering and homing in on Salazar's question: "What is the emotion that that voice is going to generate on the listener?"

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Big companies pay Jobaline to help them sift through thousands of applications to find the right workers for their hourly jobs. Human recruiters make the final judgment, but the startup determines the small pool that gets human consideration.

Jobaline says it has processed over half a million voices for positions including sales, janitorial staff and call center workers.

"In the hospitality industry, in the retail industry, you want people engaged. The average span of attention is four seconds," Salazar says.

That's very short.

The benefit of computer automation isn't just efficiency or cutting costs. Humans evaluating job candidates can get tired by the time applicant No. 25 comes through the door. Those doing the hiring can discriminate. But algorithms have stamina, and they do not factor in things like age, race, gender or sexual orientation. "That's the beauty of math," Salazar says. "It's blind."

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As a woman who has built a career on talking, I'm curious what the algorithms have to say about me. My friends say I've got two voices: the inviting, empathetic "Hey how you doing, come on over" voice. And the "Don't mess with me. I'm getting work done" voice.

Salazar ventures to guess the intrinsic quality: "I'll say it's engaging and trustworthy. I don't think it will make the bar for calming. We'll see."

The algorithms agree. They say, with 95 percent certainty, that my voice is engaging to three-quarters of Americans.

So, I'm a good fit for radio.


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