The conversation scenario is one of the most important and most challenging
scenarios for speech processing technologies because people in conversation
respond to each other in a casual style. Detecting the speech activities of
each person in a conversation is vital to downstream tasks, like natural
language processing, machine translation, etc. People refer to the detection
technology of "who speak when" as speaker diarization (SD). Traditionally,
diarization error rate (DER) has been used as the standard evaluation metric of
SD systems for a long time. However, DER fails to give enough importance to
short conversational phrases, which are short but important on the semantic
level. Also, a carefully and accurately manually-annotated testing dataset
suitable for evaluating the conversational SD technologies is still unavailable
in the speech community. In this paper, we design and describe the
Conversational Short-phrases Speaker Diarization (CSSD) task, which consists of
training and testing datasets, evaluation metric and baselines. In the dataset
aspect, despite the previously open-sourced 180-hour conversational
MagicData-RAMC dataset, we prepare an individual 20-hour conversational speech
test dataset with carefully and artificially verified speakers timestamps
annotations for the CSSD task. In the metric aspect, we design the new
conversational DER (CDER) evaluation metric, which calculates the SD accuracy
at the utterance level. In the baseline aspect, we adopt a commonly used
method: Variational Bayes HMM x-vector system, as the baseline of the CSSD
task. Our evaluation metric is publicly available at
this https URL.

文章介绍了一个新的对话说话人分离任务，即 CSSD 任务，提供了相关数据集和评估指标 CDER，并采用变分贝叶斯 HMM x-vector 系统作为 CSSD 任务的基准模型。

对话短语说话人分离任务（CSSD）：数据集，评估指标和基线

The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines

We introduce a new algorithm, called CDER, for supervised machine learning
that merges the multi-scale geometric properties of Cover Trees with the
information-theoretic properties of entropy. CDER applies to a training set of
labeled pointclouds embedded in a common Euclidean space. If typical
pointclouds corresponding to distinct labels tend to differ at any scale in any
sub-region, CDER can identify these differences in (typically) linear time,
creating a set of distributional coordinates which act as a feature extraction
mechanism for supervised learning. We describe theoretical properties and
implementation details of CDER, and illustrate its benefits on several
synthetic examples.

介绍了一种名为 CDER 的新算法，将 Cover Trees 的多尺度几何属性与熵的信息理论属性相结合，用于有标签点云的监督学习，可在通常为线性时间的情况下识别标签对应的典型点云在任何子区域、任何尺度上的差异，并创建一组分布坐标，作为特征提取机制，以提高监督学习的性能。