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.

该论文提出了一种基于编码器 - 解码器的吸引因子计算方法，该方法可灵活地生成不同数量的吸引因子，用以支持不同数量的说话人，并利用传统的自我注意力端到端神经说话人分离网络（SA-EEND）来提取讲话嵌入序列和生成说话人活动，实验结果表明，相对于传统的基于聚类的说话人分离模型和端到端模型，该方法分别在模拟的双说话人条件和未知说话人数量的条件下都取得了更好的说话人分离性能。