Due to the substantial number of clinicians, patients, and data collection
environments involved in clinical trials, gathering data of superior quality
poses a significant challenge. In clinical trials, patients are assessed based
on their speech data to detect and monitor cognitive and mental health
disorders. We propose using these speech recordings to verify the identities of
enrolled patients and identify and exclude the individuals who try to enroll
multiple times in the same trial. Since clinical studies are often conducted
across different countries, creating a system that can perform speaker
verification in diverse languages without additional development effort is
imperative. We evaluate pre-trained TitaNet, ECAPA-TDNN, and SpeakerNet models
by enrolling and testing with speech-impaired patients speaking English,
German, Danish, Spanish, and Arabic languages. Our results demonstrate that
tested models can effectively generalize to clinical speakers, with less than
2.7% EER for European Languages and 8.26% EER for Arabic. This represents a
significant step in developing more versatile and efficient speaker
verification systems for cognitive and mental health clinical trials that can
be used across a wide range of languages and dialects, substantially reducing
the effort required to develop speaker verification systems for multiple
languages. We also evaluate how speech tasks and number of speakers involved in
the trial influence the performance and show that the type of speech tasks
impacts the model performance.

使用临床试验的讲话数据进行讲话人验证对于多语言的认证和排除重复参与试验的个体至关重要，该研究评估了几个预训练的模型在不同语言环境下的表现，并展示了语音任务和参与试验的说话者数量对模型性能的影响。

临床试验中的零语种多语言说话人验证

Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials

We present the results of the Workshop on Multilingual Information Access
(MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question
answering (QA) systems in 16 typologically diverse languages. In this task, we
adapted two large-scale cross-lingual open-retrieval QA datasets in 14
typologically diverse languages, and newly annotated open-retrieval QA data in
2 underrepresented languages: Tagalog and Tamil. Four teams submitted their
systems. The best system leveraging iteratively mined diverse negative examples
and larger pretrained models achieves 32.2 F1, outperforming our baseline by
4.5 points. The second best system uses entity-aware contextualized
representations for document retrieval, and achieves significant improvements
in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.

本文介绍了 MIA 2022 workshop 关于跨语言信息检索中的开放式提取问题回答（QA）系统评估的结果，并评估了 16 种语言中的系统性能表现。通过对 14 种语言的大规模跨语言开放式提取 QA 数据集进行改编以及对 Tagalog 和 Tamil 两种语言进行新的标记，共有 4 支队伍提交了他们的系统，最佳系统借助多样化的负样本和更大的预训练模型实现了 32.2 的 F1 值，而第二名则在 Tamil 语言的检索中取得了显著的进展。

MIA 2022 共享任务：评估 16 种不同语言的跨语言开放检索问答

MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages

We have developed a method for extracting the coherence features from a
paragraph by matching similar words in its sentences. We conducted an
experiment with a parallel German corpus containing 2000 human-created and 2000
machine-translated paragraphs. The result showed that our method achieved the
best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is
compared with previous methods on various computer-generated text including
translation and paper generation (best accuracy = 67.9%, equal error rate =
32.0%). Experiments on Dutch, another rich resource language, and a low
resource one (Japanese) attained similar performances. It demonstrated the
efficiency of the coherence features at distinguishing computer-translated from
human-created paragraphs on diverse languages.

通过在句子中匹配相似单词的方法，我们开发了一种从段落中提取连贯特征的方法，实验结果表明该方法在德语、荷兰语和日语等多种语言的计算机翻译文本中表现出优异的性能，对区分人工创作和机器翻译的段落具有高效性。