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Speech in Noise

The following evaluation is for a Windows based machines. If you have a Mac OS X, you can run the evaluations by using Boot Camp or Paralells.

This test measures your ability to accurately hear speech when there’s a noisy background.

Why we measure speech in noise
The ability to detect speech in noise plays a significant role in our communication with others and our overall sense of sharpness.

Here’s an example: You’re at a restaurant, celebrating your sister’s birthday with a big group of people. It’s a boisterous crowd, and many conversations are going on around you. You want to join a conversation with two old friends sitting nearby. Can you separate the background noise from their words well enough to participate?

Another example: You’re on the phone with your boss. He’s giving you important feedback about a project you’re working on. But the static is terrible. Can you detect his words through the static well enough to follow his directions?

How we measure speech in noise
We measure “speech in noise” by comparing decibel (dB) levels.

  • If the speech in noise is at 0 dB, it means the speech and the noise are at about the same level.
  • When the speech in noise is at 15 dB, it means the speech is much louder than the noise, as it often is when you hold a conversation in a quiet library.
  • When the speech in noise is at -15 dB, it means that the speech is soft relative to the noise, such as when you try to hold a conversation on a busy street filled with traffic, construction, and other noises.

Individual performance
Each person has a point where they can accurately detect speech in noise, and a point where they no longer can. The LOWER the speech in noise level (i.e., negative numbers) reflects BETTER performance. For example, as the meter below indicates, someone who can detect speech in noise at -7.0 dB can likely carry on a conversation in a crowded restaurant. Someone who can only detect speech in noise at +7.0 probably can’t.

Click 'Continue' to enter the test and find out how well you can detect speech in noise