This paper describes our solution for the Diarization of Speaker and Language
in Conversational Environments Challenge (Displace 2023). We used a combination
of VAD for finding segfments with speech, Resnet architecture based CNN for
feature extraction from these segments, and spectral clustering for features
clustering. Even though it was not trained with using Hindi, the described
algorithm achieves the following metrics: DER 27. 1% and DER 27. 4%, on the
development and phase-1 evaluation parts of the dataset, respectively.

这篇论文描述了我们对会话环境中演讲者和语言进行辨认的解决方案，我们使用了语音活动检测、基于 Resnet 架构的 CNN 进行特征提取以及基于谱聚类的特征聚类。尽管未使用印地语进行训练，所描述的算法在数据集的开发和阶段 1 评估部分获得了 DER 分别为 27.1% 和 27.4% 的指标。

2023 年位移式演讲者脱机化挑战的系统描述

System Description for the Displace Speaker Diarization Challenge 2023

The DIarization of SPeaker and LAnguage in Conversational Environments
(DISPLACE) 2024 challenge is the second in the series of DISPLACE challenges,
which involves tasks of speaker diarization (SD) and language diarization (LD)
on a challenging multilingual conversational speech dataset. In the DISPLACE
2024 challenge, we also introduced the task of automatic speech recognition
(ASR) on this dataset. The dataset containing 158 hours of speech, consisting
of both supervised and unsupervised mono-channel far-field recordings, was
released for LD and SD tracks. Further, 12 hours of close-field mono-channel
recordings were provided for the ASR track conducted on 5 Indian languages. The
details of the dataset, baseline systems and the leader board results are
highlighted in this paper. We have also compared our baseline models and the
team's performances on evaluation data of DISPLACE-2023 to emphasize the
advancements made in this second version of the challenge.

DIarization of SPeaker and LAnguage in Conversational Environments (DISPLACE) 2024 challenge involves tasks of speaker diarization (SD), language diarization (LD), and automatic speech recognition (ASR) on a multilingual conversational speech dataset, with details of the dataset, baseline systems, and performance comparison with DISPLACE-2023.

第二届 DISPLACE 挑战赛：对话环境中的说话人和语言辨别

The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in  Conversational Environments

DER is the primary metric to evaluate diarization performance while facing a
dilemma: the errors in short utterances or segments tend to be overwhelmed by
longer ones. Short segments, e.g., `yes' or `no,' still have semantic
information. Besides, DER overlooks errors in less-talked speakers. Although
JER balances speaker errors, it still suffers from the same dilemma.
Considering all those aspects, duration error, segment error, and
speaker-weighted error constituting a complete diarization evaluation, we
propose a Balanced Error Rate (BER) to evaluate speaker diarization. First, we
propose a segment-level error rate (SER) via connected sub-graphs and adaptive
IoU threshold to get accurate segment matching. Second, to evaluate diarization
in a unified way, we adopt a speaker-specific harmonic mean between duration
and segment, followed by a speaker-weighted average. Third, we analyze our
metric via the modularized system, EEND, and the multi-modal method on real
datasets. SER and BER are publicly available at this https URL.

提出了基于平衡误差率的方法来评估说话人分离，该方法包括段错误率、片段级误差率和说话人加权误差率，其中通过使用连接的子图和自适应 IoU 阈值来获得准确的片段匹配而提出了段级错误率（SER）。