  Difficulties in following speech due to loud background sounds are common in
broadcasting. Object-based audio, e.g., MPEG-H Audio solves this problem by
providing a user-adjustable speech level. While object-based audio is gaining
momentum, transitioning to it requires time and effort. Also, lots of content
exists, produced and archived outside the object-based workflows. To address
this, Fraunhofer IIS has developed a deep-learning solution called Dialog+,
capable of enabling speech level personalization also for content with only the
final audio tracks available. This paper reports on public field tests
evaluating Dialog+, conducted together with Westdeutscher Rundfunk (WDR) and
Bayerischer Rundfunk (BR), starting from September 2020. To our knowledge,
these are the first large-scale tests of this kind. As part of one of these, a
survey with more than 2,000 participants showed that 90% of the people above 60
years old have problems in understanding speech in TV "often" or "very often".
Overall, 83% of the participants liked the possibility to switch to Dialog+,
including those who do not normally struggle with speech intelligibility.
Dialog+ introduces a clear benefit for the audience, filling the gap between
object-based broadcasting and traditionally produced material.
