  In this paper, we study how well human speech can automatically be filtered
when this overlaps with the voice and fan noise of a social robot, Pepper. We
ultimately aim for an HRI scenario where the microphone can remain open when
the robot is speaking, enabling a more natural turn-taking scheme where the
human can interrupt the robot. To respond appropriately, the robot would need
to understand what the interlocutor said in the overlapping part of the speech,
which can be accomplished by target speech extraction (TSE). To investigate how
well TSE can be accomplished in the context of the popular social robot Pepper,
we set out to manufacture a datase composed of a mixture of recorded speech of
Pepper itself, its fan noise (which is close to the microphones), and human
speech as recorded by the Pepper microphone, in a room with low reverberation
and high reverberation. Comparing a signal processing approach, with and
without post-filtering, and a convolutional recurrent neural network (CRNN)
approach to a state-of-the-art speaker identification-based TSE model, we found
that the signal processing approach without post-filtering yielded the best
performance in terms of Word Error Rate on the overlapping speech signals with
low reverberation, while the CRNN approach is more robust for reverberation.
These results show that estimating the human voice in overlapping speech with a
robot is possible in real-life application, provided that the room
reverberation is low and the human speech has a high volume or high pitch.
