  Recent diarization technologies can be categorized into two approaches, i.e.,
clustering and end-to-end neural approaches, which have different pros and
cons. The clustering-based approaches assign speaker labels to speech regions
by clustering speaker embeddings such as x-vectors. While it can be seen as a
current state-of-the-art approach that works for various challenging data with
reasonable robustness and accuracy, it has a critical disadvantage that it
cannot handle overlapped speech that is inevitable in natural conversational
data. In contrast, the end-to-end neural diarization (EEND), which directly
predicts diarization labels using a neural network, was devised to handle the
overlapped speech. While the EEND, which can easily incorporate emerging
deep-learning technologies, has started outperforming the x-vector clustering
approach in some realistic database, it is difficult to make it work for `long'
recordings (e.g., recordings longer than 10 minutes) because of, e.g., its huge
memory consumption. Block-wise independent processing is also difficult because
it poses an inter-block label permutation problem, i.e., an ambiguity of the
speaker label assignments between blocks. In this paper, we propose a simple
but effective hybrid diarization framework that works with overlapped speech
and for long recordings containing an arbitrary number of speakers. It modifies
the conventional EEND framework to simultaneously output global speaker
embeddings so that speaker clustering can be performed across blocks to solve
the permutation problem. With experiments based on simulated noisy reverberant
2-speaker meeting-like data, we show that the proposed framework works
significantly better than the original EEND especially when the input data is
long.
