  Spoken language interaction is at the heart of interpersonal communication,
and people flexibly adapt their speech to different individuals and
environments. It is surprising that robots, and by extension other digital
devices, are not equipped to adapt their speech and instead rely on fixed
speech parameters, which often hinder comprehension by the user. We conducted a
speech comprehension study involving 39 participants who were exposed to
different environmental and contextual conditions. During the experiment, the
robot articulated words using different vocal parameters, and the participants
were tasked with both recognising the spoken words and rating their subjective
impression of the robot's speech. The experiment's primary outcome shows that
spaces with good acoustic quality positively correlate with intelligibility and
user experience. However, increasing the distance between the user and the
robot exacerbated the user experience, while distracting background sounds
significantly reduced speech recognition accuracy and user satisfaction. We
next built an adaptive voice for the robot. For this, the robot needs to know
how difficult it is for a user to understand spoken language in a particular
setting. We present a prediction model that rates how annoying the ambient
acoustic environment is and, consequentially, how hard it is to understand
someone in this setting. Then, we develop a convolutional neural network model
to adapt the robot's speech parameters to different users and spaces, while
taking into account the influence of ambient acoustics on intelligibility.
Finally, we present an evaluation with 27 users, demonstrating superior
intelligibility and user experience with adaptive voice parameters compared to
fixed voice.
