cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35111/qa6w-kx44
title: First DIHARD Challenge Evaluation - SEEDLingS

description: <h3>Introduction</h3><br>
<p>First DIHARD Challenge Evaluation - SEEDLingS was developed by Duke University and the Linguistic Data Consortium (LDC) and contains approximately two hours of English child language recordings along with corresponding annotations used in support of the <a href="https://coml.lscp.ens.fr/dihard/2018/index.html">First DIHARD Challenge</a>.</p><br>
<p>The First DIHARD Challenge was an attempt to reinvigorate work on diarization through a shared task focusing on "hard" diarization; that is, speech diarization for challenging corpora where there was an expectation that existing state-of-the-art systems would fare poorly. As such, it included speech from a wide sampling of domains representing diversity in number of speakers, speaker demographics, interaction style, recording quality, and environmental conditions, including, but not limited to: clinical interviews, extended child language acquisition recordings, YouTube recordings, and conversations collected in restaurants.</p><br>
<h3>Data</h3><br>
<p>The source data was drawn from <a href="http://doi.org/10.21415/T5PK6D">SEEDLingS</a> (The Study of Environmental Effects on Developing Linguistic Skills) corpus, designed to investigate how infants' early linguistic and environmental input plays a role in their learning. Recordings were generated in the home environment of infants in the Rochester, New York area. A subset of that data was annotated by LDC for use in the First DIHARD Challenge.</p><br>
<p>This release, when combined with First DIHARD Challenge Evaluation - Nine Sources (<a href="../../../LDC2019S12">LDC2019S12</a>), contains the evaluation set audio data and annotation as well as the official scoring tool. The development data for the First DIHARD Challenge is also available from LDC as Eight Sources (<a href="../../../LDC2019S09">LDC2019S09</a>) and SEEDLingS (<a href="../../../LDC2019S10">LDC2019S10</a>).</p><br>
<p>All audio is provided in the form of 16 kHz, mono-channel FLAC files. The diarization for each recording is stored as a NIST Rich Transcription Time Marked (RTTM) file. RTTM files are space-separated text files containing one turn per line. Segmentation files are stored as HTK label files. Each of these files contains one speech segment per line. Both of the annotation file types are encoded as UTF-8. More information about the file formats is provided in the included documentation.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2019 Duke University, © 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/61f9-zg33
title: The DKU-JNU-EMA Electromagnetic Articulography Database

description: <h3>Introduction</h3><br>
<p>The DKU-JNU-EMA Electromagnetic Articulography Database was developed by <a href="https://dukekunshan.edu.cn/en">Duke Kunshan University</a> and <a href="https://english.jnu.edu.cn/">Jinan University</a> and contains approximately 10 hours of articulography and speech data in Mandarin, Cantonese, Hakka, and Teochew Chinese from two to seven native speakers for each dialect.</p><br>
<p>Electromagnetic articulography (EMA) is a method of measuring the position of parts of the mouth and their movement over time during speech and swallowing. Measurements are made from sensors placed in the mouth to capture real-time vocal tract variable trajectories. EMA is used in linguistics and language-related research to study phonetics, in particular, articulation (how sounds are made).</p><br>
<h3>Data</h3><br>
<p>Articulatory measurements were made using the <a href="https://www.ndigital.com/">NDI</a> electromagnetic articulography wave research system. Subjects had six sensors placed in various locations in their mouth and one reference sensor was placed on the bridge of their nose. For simultaneous recording of speech signals, subjects also wore a head-mounted close-talk microphone.</p><br>
<p>Speakers engaged in four different types of recording sessions: one in which they read complete sentences or short texts, and three sessions in which they read related words of a specific common consonant, vowel or tone.</p><br>
<p>Audio data is presented as single channel, 16kHz, 16-bit flac compressed wav files. Articulography data is stored as UTF-8 plain text files.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2019S14.yue.flac">Cantonese Speech</a></li><br>
<li><a href="desc/addenda/LDC2019S14.yue.tsv">Cantonese Sensor Data</a></li><br>
<li><a href="desc/addenda/LDC2019S14.yue.wco">Cantonese Parameter Data</a></li><br>
<li><a href="desc/addenda/LDC2019S14.cmn.flac">Mandarin Speech</a></li><br>
<li><a href="desc/addenda/LDC2019S14.cmn.tsv">Mandarin Sensor Data</a></li><br>
<li><a href="desc/addenda/LDC2019S14.cmn.wco">Mandarin Parameter Data</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2019 Duke Kunshan University, © 2019 Jinan University, © 2019 Trustees of the University of Pennsylvania
DOI: 10.5517/ccdc.csd.cc2498vl
title: CCDC 1975129: Experimental Crystal Structure Determination

description: Related Article: Huan He, Ruiyuan Cao, Ruidi Cao, Xiao-Yu Liu, Wei Li, Di Yu, Yuexiang Li, Miaomiao Liu, Yanmei Wu, Pingzhou Wu, Jin-Song Yang, Yunzheng Yan, Jingjing Yang, Zhi-Bing Zheng, Wu Zhong, Yong Qin|2020|Org.Biomol.Chem.|18|6155|doi:10.1039/D0OB01172J
DOI: 10.5517/ccdc.csd.cc24lc59
title: CCDC 1983850: Experimental Crystal Structure Determination

description: Related Article: Ross A. Arthurs, David L. Hughes, Christopher J. Richards|2020|Org.Biomol.Chem.|18|5466|doi:10.1039/D0OB01331E
DOI: 10.35111/11q0-p865
title: CALLFRIEND Canadian French Second Edition

description: <h3>Introduction</h3><br>
<p>CALLFRIEND Canadian French Second Edition was developed by the Linguistic Data Consortium (LDC) and consists of approximately 26 hours of unscripted telephone conversations between native speakers of Canadian French. This second edition updates the audio files to wav format, simplifies the directory structure and adds documentation and metadata. The first edition is available as CALLFRIEND Canadian French (<a href="../../../LDC96S48">LDC96S48</a>).</p><br>
<p>The CALLFRIEND series is a collection of telephone conversations in several languages conducted by LDC in support of language identification technology development. Languages covered in the collection include American English, Canadian French, Egyptian Arabic, Farsi, German, Hindi, Japanese, Korean, Mandarin Chinese, Spanish, Tamil and Vietnamese.</p><br>
<h3>Data</h3><br>
<p>All data was collected before July 1997. Participants could speak with a person of their choice on any topic; most called family members and friends. All calls originated in North America. The recorded conversations last up to 30 minutes.</p><br>
<p>The data was recorded as 8kHz u-law SPH encoded stereo files, with one end of the phone call on each channel. In this release, files were converted to WAV format, and information from the original SPH headers is described in the documentation. SPH files are not included in this second edition.</p><br>
<p>The audio files were originally split into train, dev and test folders of 20 recordings each, but they are combined in this release.</p><br>
<p>Completed calls passed through a human auditing process to verify that the target language was spoken by the participants, to check the quality of the recordings, and to record information about dialect, noise and distortion.</p><br>
<h3>Samples</h3><br>
<p>Please view the following <a href="desc/addenda/LDC2019S18.wav">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1996, 1997, 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/twqh-f096
title: Polish Speech Database

description: <h3>Introduction</h3><br>
<p>Polish Speech Database was developed by <a href="https://www.voicelab.ai/">VoiceLab</a>. It consists of 263,424 utterances of Polish speech data from 200 speakers, totaling approximately 280 hours, and corresponding transcripts.</p><br>
<p>Data collection was performed in Poland. Speakers were asked to record themselves for at least 60 minutes from their home computer using a headset while reading text on a website. The text was comprised of sentences covering most speech sounds in Polish.</p><br>
<p>The database includes speaker metadata. There were 103 male speakers and 97 female speakers. Their ages ranged from 15 years to 60 years of age. Most were in the 15-30 years age range.</p><br>
<h3>Data</h3><br>
<p>Speech data is presented as 16,000 Hz, 16-bit, single channel, flac compressed wav files. Transcripts are UTF-8 encoded plain text.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples.</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2019S19.k.flac">Female Speech</a></li><br>
<li><a href="desc/addenda/LDC2019S19.k.txt">Female Transcript</a></li><br>
<li><a href="desc/addenda/LDC2019S19.m.flac">Male Speech</a></li><br>
<li><a href="desc/addenda/LDC2019S19.m.txt">Male Transcript</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2019 VoiceLab.ai, © 2019 Trustees of the University of Pennsylvania
DOI: 10.5517/ccdc.csd.cc26lt9y
title: CCDC 2043870: Experimental Crystal Structure Determination

description: Related Article: Petter Dunås, Andrew J. Paterson, Gabriele Kociok-Köhn, Martin Rahm, Simon E. Lewis, Nina Kann|2021|Chem.Commun.|57|6518|doi:10.1039/D1CC02160E
DOI: 10.35111/bd9y-k619
title: 2016 NIST Speaker Recognition Evaluation Test Set

description: <h3>Introduction</h3><br>
<p>2016 NIST Speaker Recognition Evaluation Test Set was developed by the Linguistic Data Consortium (LDC) and NIST (National Institute of Standards and Technology). It contains approximately 340 hours of short segments of Tagalog, Cantonese, Cebuano and Mandarin telephone speech used as development and test data in the NIST-sponsored <a href="https://www.nist.gov/itl/iad/mig/speaker-recognition-evaluation-2016">2016 Speaker Recognition Evaluation (SRE)</a>.</p><br>
<p>The ongoing series of SRE yearly evaluations conducted by NIST are intended to be of interest to researchers working on the general problem of text independent speaker recognition. To this end the evaluations are designed to be simple, to focus on core technology issues, to be fully supported and to be accessible to those wishing to participate.</p><br>
<p>The SRE task is speaker detection, that is, to determine whether a specified target speaker is speaking during a given segment of speech. As in previous evaluations, SRE16 focused on telephone speech recorded over a variety of handset types for the training and test conditions. Further information about the evaluation, including some features added in SRE16, is contained in the evaluation plan included in this release.</p><br>
<h3>Data</h3><br>
<p>The telephone speech data was drawn from the Call My Net 2015 Corpus collected by LDC. Native speakers of Tagalog, Cantonese, Cebuano or Mandarin (220 unique speakers) made a total of ten telephone calls each, talking to people within their existing social networks. Speakers were encouraged to use different telephone instruments in a variety of acoustic settings and were instructed to talk for 8-10 minutes per call on a topic of their choice. All conversations were collected outside North America.</p><br>
<p>Speech data is encoded as a-law, sampled at 8kHz, and stored in SPHERE formatted files.</p><br>
<p>In addition to development and evaluation data, this corpus also contains trial lists, their associated keys, tables containing metadata information, and evaluation documentation.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2019S20.sph">speech sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2015, 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/qasz-dp17
title: CALLFRIEND American English-Non-Southern Dialect Second Edition

description: <h3>Introduction</h3><br>
<p>CALLFRIEND American English-Non-Southern Dialect Second Edition was developed by the Linguistic Data Consortium (LDC) and consists of approximately 26 hours of unscripted telephone conversations between native speakers of non-Southern dialects of American English. This second edition updates the audio files to wav format, simplifies the directory structure and adds documentation and metadata. The first edition is available as CALLFRIEND American English-Non-Southern Dialect (<a href="../../../LDC96S46">LDC96S46</a>).</p><br>
<p>The CALLFRIEND series is a collection of telephone conversations in several languages conducted by LDC in support of language identification technology development. Languages covered in the collection include American English, Canadian French, Egyptian Arabic, Farsi, German, Hindi, Japanese, Korean, Mandarin Chinese, Spanish, Tamil and Vietnamese.</p><br>
<h3>Data</h3><br>
<p>All data was collected before July 1997. Participants could speak with a person of their choice on any topic; most called family members and friends. All calls originated in North America. The recorded conversations last up to 30 minutes.</p><br>
<p>The data was recorded as 8kHz u-law SPH encoded stereo files, with one end of the phone call on each channel. In this release, files were converted to WAV format, and information from the original SPH headers is described in the documentation. SPH files are not included in this second edition.</p><br>
<p>The audio files were originally split into train, dev and test folders of 20 recordings each, but they are combined in this release.</p><br>
<p>Completed calls passed through a human auditing process to verify that the target language was spoken by the participants, to check the quality of the recordings, and to record information about dialect, noise and distortion.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2019S21.wav">audio sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1996, 1997, 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/zrz3-fw98
title: Magic Data Chinese Mandarin Conversational Speech

description: <h3>Introduction</h3><br>
<p>Magic Data Chinese Mandarin Conversational Speech was developed by <a href="https://www.magicdatatech.com/">Beijing Magic Data Technology Co., Ltd.</a> and consists of approximately 10 hours of Mandarin conversational speech from 60 speakers. Each conversation was recorded on multiple devices and is presented in multiple forms, resulting in a total of approximately 60 hours of audio with corresponding transcripts.</p><br>
<h3>Data</h3><br>
<p>All participants were native speakers of Mandarin in Mainland China from accent regions across the country. Speakers were paired for conversations on a range of topics, including travel, fitness, games, sports and pets.</p><br>
<p>Speech data was recorded on mobile devices and is presented as 16kHz, 16-bit flac compressed pcm wav. Most files are single channel; however, a stereo version of each conversation is also included.</p><br>
<p>Transcript data is contained in UTF-8 encoded plain text <a href="http://www.fon.hum.uva.nl/praat/manual/TextGrid.html">TextGrids</a>. Metadata such as topic, collection date, mobile device and speaker demographic information is found in the documentation accompanying this release.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2019S23.flac">stereo speech sample</a> and <a href="desc/addenda/LDC2019S23.txt">transcript sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2019 Beijing Magic Data Technology Co., Ltd., © 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/bbk4-8c25
title: BOLT Arabic Discussion Forum Parallel Training Data

description: <h3>Introduction</h3><br>
<p>BOLT Arabic Discussion Forum Parallel Training Data was developed by the Linguistic Data Consortium (LDC) and consists of 1,169,599 tokens of Egyptian Arabic discussion forum data collected for the DARPA BOLT program along with their corresponding English translations.</p><br>
<p>The <a href="https://www.ldc.upenn.edu/collaborations/current-projects/bolt">BOLT</a> (Broad Operational Language Translation) program developed machine translation and information retrieval for less formal genres, focusing particularly on user-generated content. LDC supported the BOLT program by collecting informal data sources -- discussion forums, text messaging and chat -- in Chinese, Egyptian Arabic and English. The collected data was translated and annotated for various tasks including word alignment, treebanking, propbanking and co-reference.</p><br>
<h3>Data</h3><br>
<p>The source data in this release consists of discussion forum threads harvested from the Internet by LDC using a combination of manual and automatic processes. The full source data collection is released as BOLT Arabic Discussion Forums (<a href="../../../LDC2018T10">LDC2018T10</a>).</p><br>
<p>Data was manually selected for translation according to several criteria, including linguistic features and topic features. The files were then segmented into sentence units, formatted into a human-readable translation format and assigned to translation vendors. Translators followed LDC's BOLT translation guidelines. Bilingual LDC staff performed quality control procedures on the completed translations.</p><br>
<p>All data are presented as UTF-8. The following table shows the data volume of this package:</p><br>
<table border="1" cellpadding="5"><br>
<tbody><br>
<tr><br>
<td>Source Lang</td><br>
<td>Genre</td><br>
<td>Files</td><br>
<td>Source Tokens</td><br>
</tr><br>
<tr><br>
<td>Arabic</td><br>
<td>Discussion Forum</td><br>
<td>2,651</td><br>
<td>1,169,599</td><br>
</tr><br>
</tbody><br>
</table><br>
<h3>Acknowledgement</h3><br>
<p>This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-11-C-0145. The content does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2019T01.arz.xml">Egyptian Arabic sample</a> and <a href="desc/addenda/LDC2019T01.eng.xml">English sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/cx5x-g862
title: DEFT Chinese Committed Belief Annotation

description: <h3>Introduction</h3><br>
<p>DEFT Chinese Committed Belief Annotation was developed by the Linguistic Data Consortium (LDC) and consists of approximately 83,000 tokens of Chinese discussion forum text annotated for "committed belief," which marks the level of commitment displayed by the author to the truth of the propositions expressed in the text.</p><br>
<p>DARPA's Deep Exploration and Filtering of Text (DEFT) program aimed to address remaining capability gaps in state-of-the-art natural language processing technologies related to inference, causal relationships and anomaly detection. LDC supported the DEFT program by collecting, creating and annotating a variety of language resources.</p><br>
<p>LDC has also released DEFT Spanish Committed Belief Annotation (<a href="../../../LDC2019T09">LDC2019T09</a>).</p><br>
<h3>Data</h3><br>
<p>The source data is Chinese discussion forum web text collected by LDC. Annotations fall into one of four categories: committed belief, non-committed belief, reported belief and not applicable. Further information about the annotation methodology is contained in the documentation accompanying this release.</p><br>
<p>This publication contains 140 files (83,016 tokens). Annotation files are stored in XML format, and source documents are stored in plain text format. Both types of files are encoded in UTF-8.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2019T03.src.xml">source sample</a> and <a href="desc/addenda/LDC2019T03.ann.xml">annotation sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p><br>
<h3>Acknowledgement</h3><br>
<p>This material is based on research sponsored by Air Force Research Laboratory and Defense Advance Research Projects Agency under agreement number FA8750-13-2-0045. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory and Defense Advanced Research Projects Agency or the U.S. Government.</p></br>
Portions © 2019 Trustees of the University of Pennsylvania
DOI: 10.35111/ac9t-pw34
title: Multilingual ATIS

description: <h3>Introduction</h3><br>
<p>Multilingual ATIS was developed by Google Inc. and consists of 5,871 utterances from ATIS2 (<a href="../../../LDC93S5">LDC93S5</a>), ATIS3 Training Data (<a href="../../../LDC94S19">LDC94S19</a>), and ATIS3 Test Data (<a href="../../../LDC95S26">LDC95S26</a>) annotated and translated into Hindi and Turkish.</p><br>
<p>The ATIS (Air Travel Information Services) collection was developed to support the research and development of speech understanding systems. Participants were presented with various hypothetical travel planning scenarios and asked to solve them by interacting with partially or completely automated ATIS systems. The resulting utterances were recorded and transcribed. Data was collected in the early 1990s at five US sites: Raytheon BBN, Carnegie Mellon University, MIT Laboratory for Computer Science, National Institute for Standards and Technology and SRI International.</p><br>
<h3>Data</h3><br>
<p>The data in this release is separated into training and test sets following the original ATIS division. The training set contains 4978 utterances selected from the Class A (context independent) training data in the ATIS2 and ATIS3 corpora. The test set contains 893 utterances from the November 1993 and December 1994 data sets in ATIS3.</p><br>
<p>The original English utterances were manually translated into Hindi and Turkish. This release also includes the original English utterance and the machine translation back into English of the manual target language utterance translation.</p><br>
<p>Each utterance is annotated with named entities via table lookup; markers include city, airline, airport names, and dates.</p><br>
<p>All data is stored in UTF-8 encoded tab separated value files.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2019T04.txt">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2019 Google Inc., © 1993-1995, 2019 Trustees of the University of Pennsylvania