  This paper investigates an end-to-end neural diarization (EEND) method for an
unknown number of speakers. In contrast to the conventional cascaded approach
to speaker diarization, EEND methods are better in terms of speaker overlap
handling. However, EEND still has a disadvantage in that it cannot deal with a
flexible number of speakers. To remedy this problem, we introduce
encoder-decoder-based attractor calculation module (EDA) to EEND. Once
frame-wise embeddings are obtained, EDA sequentially generates speaker-wise
attractors on the basis of a sequence-to-sequence method using an LSTM
encoder-decoder. The attractor generation continues until a stopping condition
is satisfied; thus, the number of attractors can be flexible. Diarization
results are then estimated as dot products of the attractors and embeddings.
The embeddings from speaker overlaps result in larger dot product values with
multiple attractors; thus, this method can deal with speaker overlaps. Because
the maximum number of output speakers is still limited by the training set, we
also propose an iterative inference method to remove this restriction. Further,
we propose a method that aligns the estimated diarization results with the
results of an external speech activity detector, which enables fair comparison
against cascaded approaches. Extensive evaluations on simulated and real
datasets show that EEND-EDA outperforms the conventional cascaded approach.
