  Overlapped speech detection (OSD) is critical for speech applications in
scenario of multi-party conversion. Despite numerous research efforts and
progresses, comparing with speech activity detection (VAD), OSD remains an open
challenge and its overall performance is far from satisfactory. The majority of
prior research typically formulates the OSD problem as a standard
classification problem, to identify speech with binary (OSD) or three-class
label (joint VAD and OSD) at frame level. In contrast to the mainstream, this
study investigates the joint VAD and OSD task from a new perspective. In
particular, we propose to extend traditional classification network with
multi-exit architecture. Such an architecture empowers our system with unique
capability to identify class using either low-level features from early exits
or high-level features from last exit. In addition, two training schemes,
knowledge distillation and dense connection, are adopted to further boost our
system performance. Experimental results on benchmark datasets (AMI and
DIHARD-III) validated the effectiveness and generality of our proposed system.
Our ablations further reveal the complementary contribution of proposed
schemes. With $F_1$ score of 0.792 on AMI and 0.625 on DIHARD-III, our proposed
system outperforms several top performing models on these datasets, but also
surpasses the current state-of-the-art by large margins across both datasets.
Besides the performance benefit, our proposed system offers another appealing
potential for quality-complexity trade-offs, which is highly preferred for
efficient OSD deployment.
