  In their everyday life, the speech recognition performance of human listeners
is influenced by diverse factors, such as the acoustic environment, the talker
and listener positions, possibly impaired hearing, and optional hearing
devices. Prediction models come closer to considering all required factors
simultaneously to predict the individual speech recognition performance in
complex acoustic environments. While such predictions may still not be
sufficiently accurate for serious applications, they can already be performed
and demand an accessible representation. In this contribution, an interactive
representation of speech recognition performance is proposed, which focuses on
the listeners head orientation and the spatial dimensions of an acoustic scene.
A exemplary modeling toolchain, including an acoustic rendering model, a
hearing device model, and a listener model, was used to generate a data set for
demonstration purposes. Using the spatial speech recognition maps to explore
this data set demonstrated the suitability of the approach to observe possibly
relevant behavior. The proposed representation provides a suitable target to
compare and validate different modeling approaches in ecologically relevant
contexts. Eventually, it may serve as a tool to use validated prediction models
in the design of spaces and devices which take speech communication into
account.
