Technology is constantly evolving and bringing us innovative new hiring techniques, the latest of which is voice profiling. This savvy practice allows you to use computer-based algorithms to predict a person’s fit for the job based on an analysis of their voice. The theory behind voice profiling is that the voice is kind of like a fingerprint in that can predict the way a person will interact with your team and their level of performance.
One voice profiling tool called Jobaline, strives to foresee “the emotion that that voice is going to generate on the listener.” Basically, candidates with an attractive voice that’s pleasing to the ear would make it to the next round of interviews, while those with less-than-lovely voices would be eliminated.
How Voice Profiling Works
There is actually a defined formula that can be used to test the accuracy of the voice profiling methodology. The three steps include:
- Measuring the characteristics of candidates’ voices
- Gauging the reactions of listeners
- Determining whether or not listeners’ reactions are related to positive organizational outcomes
To gain accurate results, findings of both rounds one and two and two and three would need to be correlated. If a pattern is detected, the voice profiling method can be considered useful in identifying and selecting top performers. Currently, companies have only been able to gauge the relationship between steps one and two, which isn’t enough to base a hiring decision on.
If voice profiling is going to have an impact on hiring decisions, the emotional reactions of listeners must demonstrate higher levels of performance. Basically, instead of just using technology to identify the physical properties that make some voices sound better than others, it will need to prove whether these attributes can actually benefit the company.
A number of questions about voice profiling must still be answered, including the level of consistency of a person’s voice profile, the uniformity of emotional reactions to a particular voice pattern and the significance of the voice patterns identified by technology.
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