  End-to-end speaker diarization for an unknown number of speakers is addressed
in this paper. Recently proposed end-to-end speaker diarization outperformed
conventional clustering-based speaker diarization, but it has one drawback: it
is less flexible in terms of the number of speakers. This paper proposes a
method for encoder-decoder based attractor calculation (EDA), which first
generates a flexible number of attractors from a speech embedding sequence.
Then, the generated multiple attractors are multiplied by the speech embedding
sequence to produce the same number of speaker activities. The speech embedding
sequence is extracted using the conventional self-attentive end-to-end neural
speaker diarization (SA-EEND) network. In a two-speaker condition, our method
achieved a 2.69 % diarization error rate (DER) on simulated mixtures and a 8.07
% DER on the two-speaker subset of CALLHOME, while vanilla SA-EEND attained
4.56 % and 9.54 %, respectively. In unknown numbers of speakers conditions, our
method attained a 15.29 % DER on CALLHOME, while the x-vector-based clustering
method achieved a 19.43 % DER.
