Does Deep Neural Networks (DNN) separation of voices help hearing aid users segregate speakers?

We are the first to show a clear enhancement in performance when hearing impaired listeners are being presented with voices separated by means of DNN.

The Deep Neural Networks algorithms (DNN) can recognize and separate known voices and thereby help the user segregate two different speakers. While our collaborator Tampere University has the expertise on developing the algorithms that make the voice separation possible, Eriksholm Research Centre was able run tests to find out whether the algorithm worked the same way for hearing aid users in real life as expected in theory.

To do so, the test subjects needed to pay attention to two voices speaking at the same time – a new test based on the so-called Danish Hearing In Noise Test (HINT). The tests showed that when DNN is used to separate the voices, the test subjects performed better than when the DNN was not used. This is a very important result, since it has never before been documented.

Listen to the sound bites from the test

To hear the difference between the two sound clips as clear as possible, please put on head phones.

Watch our Facebook Live lecture with Lars Bramsløw

Research engineer did a Facebook Live lecture on segregation of voices using DNN in December 2017. Watch it here.

Further reading:

Bramsløw L, Naithani G, Hafez A, Barker T, Pontoppidan NH, Virtanen T (2018). Improving competing voices segregation for hearing impaired listeners using a low-latency deep neural network (submitted). The Journal of the Acoustical Society of America.