This paper describes the TSUP team's submission to the ISCSLP 2022
conversational short-phrase speaker diarization (CSSD) challenge which
particularly focuses on short-phrase conversations with a new evaluation metric
called conversational diarization error rate (CDER). In this challenge, we
explore three kinds of typical speaker diarization systems, which are spectral
clustering(SC) based diarization, target-speaker voice activity
detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our
major findings are summarized as follows. First, the SC approach is more
favored over the other two approaches under the new CDER metric. Second, tuning
on hyperparameters is essential to CDER for all three types of speaker
diarization systems. Specifically, CDER becomes smaller when the length of
sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP
will worsen the CDER metric on the challenge data. Our submitted SC system
eventually ranks the third place in the challenge.

该论文描述了 TSUP 团队在 ISCSLP 2022 CSSD 挑战赛中的参赛表现和关键研究成果，侧重于短话语音者分离与新的评价指标 CDER。SC 方法是最受欢迎的方法并且所有的三种语音者分离系统都需要调参，最后 SC 系统的表现居于第三名。

面向短语对话扬声器辨别挑战的 TSUP 扬声器分离系统

TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge

A method to perform offline and online speaker diarization for an unlimited
number of speakers is described in this paper. End-to-end neural diarization
(EEND) has achieved overlap-aware speaker diarization by formulating it as a
multi-label classification problem. It has also been extended for a flexible
number of speakers by introducing speaker-wise attractors. However, the output
number of speakers of attractor-based EEND is empirically capped; it cannot
deal with cases where the number of speakers appearing during inference is
higher than that during training because its speaker counting is trained in a
fully supervised manner. Our method, EEND-GLA, solves this problem by
introducing unsupervised clustering into attractor-based EEND. In the method,
the input audio is first divided into short blocks, then attractor-based
diarization is performed for each block, and finally, the results of each block
are clustered on the basis of the similarity between locally-calculated
attractors. While the number of output speakers is limited within each block,
the total number of speakers estimated for the entire input can be higher than
the limitation. To use EEND-GLA in an online manner, our method also extends
the speaker-tracing buffer, which was originally proposed to enable online
inference of conventional EEND. We introduce a block-wise buffer update to make
the speaker-tracing buffer compatible with EEND-GLA. Finally, to improve online
diarization, our method improves the buffer update method and revisits the
variable chunk-size training of EEND. The experimental results demonstrate that
EEND-GLA can perform speaker diarization of an unseen number of speakers in
both offline and online inferences.

本研究提出了一种新的音频说话人分离方法 --EEND-GLA, 它结合了声源吸引点和无监督聚类，能够在离线和在线推理中对未知数量的讲话人进行说话人分离，实验结果表明其分离效果良好。

使用全局和局部引力对无限数量的发言人进行在线神经分离

Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors

This paper investigates a method for simulating natural conversation in the
model training of end-to-end neural diarization (EEND). Due to the lack of any
annotated real conversational dataset, EEND is usually pretrained on a
large-scale simulated conversational dataset first and then adapted to the
target real dataset. Simulated datasets play an essential role in the training
of EEND, but as yet there has been insufficient investigation into an optimal
simulation method. We thus propose a method to simulate natural conversational
speech. In contrast to conventional methods, which simply combine the speech of
multiple speakers, our method takes turn-taking into account. We define four
types of speaker transition and sequentially arrange them to simulate natural
conversations. The dataset simulated using our method was found to be
statistically similar to the real dataset in terms of the silence and overlap
ratios. The experimental results on two-speaker diarization using the CALLHOME
and CSJ datasets showed that the simulated dataset contributes to improving the
performance of EEND.

研究了一种用于模拟自然对话的方法，用于训练端到端神经音频分离技术（EEND），并提出了一种考虑交替对话方式的模拟方法，用于在缺乏真实语音数据的情况下，优化 EEND 模型的训练。通过对 CALLHOME 和 CSJ 数据集的实验结果表明，使用这种方法模拟数据有助于提高 EEND 的性能表现。