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

We propose two improvements to target-speaker voice activity detection
(TS-VAD), the core component in our proposed speaker diarization system that
was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription
(M2MeT) challenge. These techniques are designed to handle multi-speaker
conversations in real-world meeting scenarios with high speaker-overlap ratios
and under heavy reverberant and noisy condition. First, for data preparation
and augmentation in training TS-VAD models, speech data containing both real
meetings and simulated indoor conversations are used. Second, in refining
results obtained after TS-VAD based decoding, we perform a series of
post-processing steps to improve the VAD results needed to reduce diarization
error rates (DERs). Tested on the ALIMEETING corpus, the newly released
Mandarin meeting dataset used in M2MeT, we demonstrate that our proposed system
can decrease the DER by up to 66.55/60.59% relatively when compared with
classical clustering based diarization on the Eval/Test set.

在多频道多方会议转录挑战中，我们提出了两种改进目标说话人语音活动检测技术，能够处理高重叠率，重音和嘈杂环境中的多人对话，并展示了我们的系统与经典聚类算法相比，在 ALIMEETING 语料库中将 DER 降低了长达 66.55/60.59%。