five

IWSLT 2022-2023 Shared Task Training, Development and Test Set

收藏
DataCite Commons2025-06-05 更新2026-05-03 收录
下载链接:
http://catalog.ldc.upenn.edu/LDC2025S05
下载链接
链接失效反馈
官方服务:
资源简介:
<h3>Introduction</h3> <p>IWSLT 2022 - 2023 Shared Task Training, Development and Test Set (LDC2025S05) was developed by the Linguistic Data Consortium (LDC). It contains 210 hours of Tunisian Arabic conversational telephone speech, transcripts and their English translations covering 175 hours of that speech, speaker metadata, and documentation. This material constitutes the training, development and test data used in the International Conference on Spoken Language Translation (IWSLT) <a href="https://iwslt.org/2022/dialect">Dialectal Speech Translation task (2022)</a> and the <a href="https://iwslt.org/2023/low-resource">Dialectal and Low-resource track (2023)</a>.</p> <h3>Data</h3> <p>The telephone speech was collected by LDC in 2016-2017 from native speakers of Tunisian Arabic in Tunis. Speakers were recruited to make telephone calls to people in their social networks from a variety of noise conditions and handsets. The calls were recorded using a robot operator system that captured digital audio samples directly from the regional public telephone network with the informed consent of participants. The audio files are two-channel recordings across 1,188 conversations.</p> <p>Transcripts are orthographic following <a href="../../../LDC2004L02">Buckwalter</a> transliteration. IPA (International Phonetic Alphabet) transcripts were added to a subset of the data. All transcribed segments were translated into English. Further information on the transcription and translation methodologies is contained in the documentation accompanying this release.</p> <p>Speech data is presented as FLAC-compressed MS-WAV files in 16-bit 8 kHz PCM format. All text data is UTF-8 encoded.</p> <h3>Updates</h3> <p>No updates at this time.</p>
提供机构:
Linguisitc Data Consortium
创建时间:
2025-06-03
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作