cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35111/m2cs-3954
title: MWE-Aware English Dependency Corpus 2.0

description: <h3>Introduction</h3><br>
<p>MWE-Aware English Dependency Corpus Version 2.0 was developed by the <a href="http://isw3.naist.jp/Contents/Research/mi-01-en.html">Nara Institute of Science and Technology Computational Linguistics Laboratory</a> and consists of English compound function words annotated in dependency format. The data is derived from OntoNotes Release 5.0 (<a href="../../../LDC2013T19">LDC2013T19</a>).</p><br>
<p>Compound functions words are a type of multiword expression (MWE). MWEs are groups of tokens that can be treated as a single semantic or syntactic unit. Doing so facilitates natural language processing tasks such as constituency and dependency parsing.</p><br>
<p>Version 2.0 adds annotations of named entities (persons, locations, organizations) into dependency trees that are aware of compound function words. Version 1.0 is available from LDC as MWE-Aware English Dependency Corpus (<a href="../../../LDC2017T01">LDC2017T01</a>).</p><br>
<h3>Data</h3><br>
<p>MWE-Aware English Dependency Corpus Version 2.0 was derived from the Wall Street Journal portion of OntoNotes Release 5.0. MWEs were identified in OntoNotes' phrase structure trees and each MWE was established as a single subtree. Those phrase structure subtrees were then converted to a dependency structure (<a href="http://nlp.stanford.edu/software/stanford-dependencies.shtml">the Stanford dependencies</a>) in <a href="http://universaldependencies.org/format.html">CoNLL format</a>.</p><br>
<p>The data is split into 1,728 phrase structure trees as *.parse files and a single 14-column tab separated dependency as a *.conll file. Both file types are encoded as UTF-8.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2017T16.txt">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1987-1989 Dow Jones & Company, Inc. © 2017 NAIST Computational Linguistics Laboratory, © 2007, 2008, 2009, 2011, 2013, 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/20f8-x526
title: GALE Phase 4 Chinese Broadcast News Transcripts

description: <h3>Introduction</h3><br>
<p>GALE Phase 4 Chinese Broadcast News Transcripts was developed by the Linguistic Data Consortium (LDC) and contains transcriptions of approximately 134 hours of Chinese broadcast news speech collected in 2008 by LDC and Hong University of Science and Technology (HKUST), Hong Kong, during Phase 4 of the DARPA GALE (Global Autonomous Language Exploitation) Program.</p><br>
<p>Corresponding audio data is released as GALE Phase 4 Chinese Broadcast News Speech (<a href="../../../LDC2017S25">LDC2017S25</a>).</p><br>
<p>The broadcast news recordings feature news broadcasts focusing principally on current events from the following sources: China Central TV (CCTV), a national and international broadcaster in Mainland China; Phoenix TV, a Hong Kong-based satellite television station; and Voice of America (VOA), a U.S. government-funded broadcast programmer.</p><br>
<h3>Data</h3><br>
<p>The transcript files are in plain-text, tab-delimited format (TDF) with UTF-8 encoding, and the transcribed data totals 1,696,879 tokens. The transcripts were created with the LDC-developed transcription tool, XTrans, a multi-platform, multilingual, multi-channel transcription tool that supports manual transcription and annotation of audio recordings. XTrans is available from the following link, <a href="https://www.ldc.upenn.edu/language-resources/tools/xtrans">https://www.ldc.upenn.edu/language-resources/tools/xtrans</a>.</p><br>
<p>The files in this corpus were transcribed by LDC staff and/or by transcription vendors under contract to LDC. Transcribers followed LDC's quick transcription guidelines (QTR) and quick rich transcription specification (QRTR) both of which are included in the documentation with this release. QTR transcription consists of quick (near-) verbatim, time-aligned transcripts plus speaker identification with minimal additional mark-up. It does not include sentence unit annotation. QRTR annotation adds structural information such as topic boundaries and manual sentence unit annotation to the core components of a quick transcript. Files with QTR as part of the filename were developed using QTR transcription. Files with QRTR in the filename indicate QRTR transcription.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2017T18.txt">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p><br>
<h3>Acknowledgement</h3><br>
<p>This work was supported in part by the Defense Advanced Research Projects Agency, GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.</p></br>
Portions © 2008 China Central TV, Phoenix TV, © 2008, 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/4a2f-0j88
title: UCLA High-Speed Laryngeal Video and Audio

description: <h3>Introduction</h3><br>
<p>UCLA High-Speed Laryngeal Video and Audio was developed by <a href="http://www.seas.ucla.edu/spapl/index.html">UCLA Speech Processing and Auditory Perception Laboratory</a> and is comprised of high-speed laryngeal video recordings of the vocal folds and synchronized audio recordings from nine subjects collected between April 2012 and April 2013. Speakers were asked to sustain the vowel /i/ for approximately ten seconds while holding voice quality, fundamental frequency, and loudness as steady as possible.</p><br>
<p>In the field of speech production theory, data such as contained in this release may be used to study the relationship between vocal folds vibration and resulting voice quality.</p><br>
<h3>Data</h3><br>
<p>None of the subjects had a history of a voice disorder. There was no native language requirement for recruiting subjects; participants were native speakers of various languages, including English, Mandarin Chinese, Taiwanese Mandarin, Cantonese and German.</p><br>
<p>Audio data is presented as 16kHz 16-bit flac and video is in avi format at 5 fps (frames per second).</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2017V01.avi">video sample</a> and this <a href="desc/addenda/LDC2017V01.flac">audio sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 Abeer Alwan, © 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/2j6c-6z19
title: DIRHA English WSJ Audio

description: <h3>Introduction</h3><br>
<p>DIRHA English WSJ Audio was developed as part of the <a href="https://dirha.fbk.eu/">Distant-Speech Interaction for Robust Home Applications (DIRHA) Project</a> which addressed natural spontaneous speech interaction with distant microphones in a domestic environment. It is comprised of approximately 85 hours of real and simulated read speech by six native American English speakers. The target utterances were taken from CSR-I (WSJ0) Complete (<a href="../../../LDC93S6A/">LDC93S6A</a>), specifically, the 5,000 word subset of read speech from Wall Street Journal news text.</p><br>
<p>This release contains signals of different characteristics in terms of noise and reverberation making it suitable for various multi-microphone signal processing and distant speech recognition tasks. The corpus can be coupled with related Kaldi baselines and tools that are available <a href="https://github.com/SHINE-FBK/DIRHA_English_wsj">here</a>.</p><br>
<h3>Data</h3><br>
<p>Speech was collected in a real apartment setting with typical domestic background noise and inter/intra-room reverberation effects. A total of 32 microphones were placed in the living-room (26 microphones) and in the kitchen (6 microphones). The original recordings were made at a sampling frequency of 48 kHz. However, for the sake of compactness, the released signals in this publication are in wav format with 16 kHz sampling frequency and 16 bit resolution.</p><br>
<p>Annotations for each acoustic sequence are included in xml format, such as microphone positions, speaker id, speaker gender and speaker position. Additional metadata about the speakers and images of the apartment setting are also provided. Consult the documentation accompanying this release for more information about the collection.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2018S01.wav">audio sample</a> and <a href="desc/addenda/LDC2018S01.xml">annotation sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1987-1989 Dow Jones & Company, Inc., © 2018 Fondazione Bruno Kessler, © 1996, 2018 Trustees of the University of Pennsylvania
DOI: 10.35111/1e1m-dn11
title: Rhythm and Pitch

description: <h3>Introduction</h3><br>
<p>Rhythm and Pitch contains approximately 27 minutes of spontaneous English conversations and radio news stories annotated with the Rhythm and Pitch (RaP) scheme. Speech data for annotation was taken from two corpora released by LDC, CALLHOME American English Speech (<a href="../../../LDC97S42">LDC97S42</a>) and Boston University Radio Speech Corpus (<a href="../../../LDC96S36">LDC96S36</a>).</p><br>
<p>The RaP system permits the capture of both intonational and rhythmic aspects of speech. Four labeling tiers are used for annotating speech prosody. These tiers carry information about the syllabic organization and orthography of the speech, its rhythmic structure, tonal patterns, and other information. More information about the RaP system is available on the <a href="http://tedlab.mit.edu/tedlab_website/RaPHome.html">RaP homepage</a>.</p><br>
<h3>Data</h3><br>
<p>Speech data are presented as flac compressed 16-bit wav files. The Boston data are one channel 16kHz files, while the CALLHOME data are either one or two channel 8kHz files. Annotations are UTF-8 encoded <a href="http://www.fon.hum.uva.nl/praat/">Praat</a> TextGrids.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2018S04.flac">audio sample</a> and <a href="desc/addenda/LDC2018S04.txt">annotation sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2018 Mara Breen, Laura C. Dilley, Meredith Brown, Edward A.F. Gibson, © 1996, 1997, 2018 Trustees of the University of Pennsylvania
DOI: 10.35111/rmba-9w42
title: CALLFRIEND Mandarin Chinese-Mainland Dialect Second Edition

description: <h3>Introduction</h3><br>
<p>CALLFRIEND Mandarin Chinese-Mainland Dialect Second Edition was developed by the Linguistic Data Consortium (LDC) and consists of approximately 24 hours of unscripted telephone conversations between native speakers of the Mandarin Chinese dialect spoken in mainland China. 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 Mandarin Chinese-Mainland Dialect (<a href="../../../LDC96S55">LDC96S55</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 two human audits. The first audit was conducted to verify that the target language was spoken by the participants and to check the quality of the recordings. The second audit was conducted by a native speaker familiar with Mainland and Taiwanese Mandarin dialects to classify the conversations under one of the two categories.</p><br>
<h3>Samples</h3><br>
<p>Please listen to this <a href="desc/addenda/LDC2018S09.wav">sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1996, 1997, 2018 Trustees of the University of Pennsylvania
DOI: 10.35111/rfmw-n126
title: CIEMPIESS Balance

description: <h3>Introduction</h3><br>
<p>CIEMPIESS (Corpus de Investigaci&oacute;n en Espa&ntilde;ol de M&eacute;xico del Posgrado de Ingenier&iacute;a El&eacute;ctrica y Servicio Social) Balance was developed by the Development of Speech Technologies program at the <a href="http://www.ingenieria.unam.mx">School of Engineering</a> at the <a href="http://www.unam.mx/">National Autonomous University of Mexico</a> (UNAM) and consists of approximately 18 hours of Mexican Spanish broadcast speech with associated transcripts. The goal of this work was to create acoustic models for automatic speech recognition. For more information and documentation see the <a href="http://www.CIEMPIESS.org/">CIEMPIESS-UNAM Project website</a>.</p><br>
<p>CIEMPIESS Balance is a companion corpus to CIEMPIESS Light, released by LDC as <a href="../../../LDC2017S23">LDC2017S23</a>. It was developed so that the data sets together constitute a gender-balanced corpus. The gender breakdown in CIEMPIESS Light is approximately 75% male and 25% female. In CIEMPIESS Balance the gender breakdown is approximately 25% male and 75% female.</p><br>
<p>LDC has released the following data sets in the CIEMPIESS series:</p><br>
<ul><br>
<li>CIEMPIESS (<a href="../../../LDC2015S07">LDC2015S07</a>)</li><br>
<li>CHM150 (<a href="../../../LDC2016S04">LDC2016S04</a>)</li><br>
<li>CIEMPIESS Light (<a href="../../../LDC2017S23">LDC2017S23</a>)</li><br>
<li>CIEMPIESS Experimentation (<a href="../../../LDC2019S07">LDC2019S07</a>)</li><br>
</ul><br>
<h3>Data</h3><br>
<p>The majority of the speech recordings were collected from <a href="http://www.derecho.unam.mx/cultura-juridica/radio.php">Radio-IUS</a>, a UNAM radio station. Other recordings were taken from <a href="https://www.youtube.com/user/DEDUNAM/videos">IUS Canal Multimedia</a> and <a href="https://www.youtube.com/channel/UCTxkzdUd0tiXT5BN5o6Xo-A/videos">Centro Universitario de Estudios Jur&iacute;dicos</a> (CUEJ UNAM). These two channels feature videos with speech around legal issues and topics related to UNAM.</p><br>
<p>The audio files are presented as 16 kHz, 16-bit PCM flac format for this release. Transcripts are presented as UTF-8 encoded plain text.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2018S11.flac">speech sample</a> and <a href="desc/addenda/LDC2018S11.txt">transcript sample</a>.</p><br>
<h3>Acknowledgements</h3><br>
<p>The authors would like to thank to Alejandro V. Mena, Elena Vera and Ang&eacute;lica Guti&eacute;rrez for their support to the social service program: "Desarrollo de Tecnolog&iacute;as del Habla." They also thank the social service students for their hard work.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2018 Carlos Daniel Hernández Mena, © 2018 Universidad Nacional Autónoma de México, © 2018 Trustees of the University of Pennsylvania
DOI: 10.35111/yfn1-4402
title: Multi-Language Conversational Telephone Speech 2011 -- Spanish

description: <h3>Introduction</h3><br>
<p>Multi-Language Conversational Telephone Speech 2011 -- Spanish was developed by the Linguistic Data Consortium (LDC) and is comprised of approximately 23 hours of telephone speech in Spanish.</p><br>
<p>The data were collected primarily to support research and technology evaluation in automatic language identification, and portions of these telephone calls were used in the NIST 2011 Language Recognition Evaluation (<a href="https://www.nist.gov/itl/iad/mig/2011-language-recognition-evaluation">LRE</a>). LRE 2011 focused on language pair discrimination for 24 languages/dialects, some of which could be considered mutually intelligible or closely related.</p><br>
<p>LDC has also released the following as part of the Multi-Language Conversational Telephone Speech 2011 series:</p><br>
<ul><br>
<li>Slavic Group (<a href="../../../LDC2016S11">LDC2016S11</a>)</li><br>
<li>Turkish (<a href="../../../LDC2017S09">LDC2017S09</a>)</li><br>
<li>South Asian (<a href="../../../LDC2017S14">LDC2017S14</a>)</li><br>
<li>Central Asian (<a href="../../../LDC2018S03">LDC2018S03</a>)</li><br>
<li>Central European (<a href="../../../LDC2018S08">LDC2018S08</a>)</li><br>
<li>Arabic (<a href="../../../LDC2019S02">LDC2019S02</a>)</li><br>
<li>English (<a href="../../../LDC2019S06">LDC2019S06</a>)</li><br>
</ul><br>
<h3>Data</h3><br>
<p>Participants were recruited by native speakers who contacted acquaintances in their social network. Those native speakers made one call, up to 15 minutes, to each acquaintance. The data was collected using <a href="https://www.ldc.upenn.edu/about/facilities/human-subjects-collection">LDC's telephone collection infrastructure</a>, comprised of three computer telephony systems. Human auditors labeled calls for callee gender, dialect type and noise. Demographic information about the participants was not collected.</p><br>
<p>All audio data are presented in FLAC-compressed MS-WAV (RIFF) file format (*.flac); when uncompressed, each file is 2 channels, recorded at 8000 samples/second with samples stored as 16-bit signed integers, representing a lossless conversion from the original mu-law sample data as captured digitally from the public telephone network. The following table summarizes the total number of calls, total number of hours of recorded audio, and the total size of compressed data:</p><br>
<table><br>
<tbody><br>
<tr><br>
<td>group</td><br>
<td>lng</td><br>
<td>#calls</td><br>
<td>#hours</td><br>
<td>#MB</td><br>
</tr><br>
<tr><br>
<td>spanish</td><br>
<td>spa</td><br>
<td>125</td><br>
<td>23.6</td><br>
<td>1148</td><br>
</tr><br>
</tbody><br>
</table><br>
<h3>Samples</h3><br>
<p>Please listen to this <a href="desc/addenda/LDC2018S12.flac">audio sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2018 Trustees of the University of Pennsylvania
DOI: 10.35111/kvd2-8a82
title: AISHELL-1

description: <h3>Introduction</h3><br>
<p>AISHELL-1 was developed by <a href="http://www.aishelltech.com/sy">Beijing Shell Shell Technology Co., Ltd.</a> It contains approximately 520 hours of Chinese Mandarin speech from 400 speakers recorded simultaneously on three different devices with associated transcripts.</p><br>
<p>The goal of the collection was to support speech recognition system development in 11 domains, five of which are include in this corpus: Finance, Science & Technology, Sports, Entertainment, and News. Participants read 500 sentences covering the domains; sentences were chosen for their speech and phonetic characteristics.</p><br>
<p>Speakers were recruited from different accent areas across China, including North, South and Yue-Gui-Min regions. There were 214 female speakers and 186 male speakers, constituting 53% and 47% of the database, respectively. Additional demographic information about the participants is included in this release.</p><br>
<h3>Data</h3><br>
<p>Speech was recorded in a quiet indoor environment on a high fidelity microphone and two mobile phones (Android and iOS). All speech is presented as 16-bit flac compressed wav files; the microphone speech sample rate is 44.1kHz and the phone speech sample rate is 16kHz. Each speech file ranges from approximately 1 second to 14 seconds in length.</p><br>
<p>Transcripts are stored as UTF-8 encoded plain text files and are not time-aligned.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2018S14.mic.flac">Microphone</a></li><br>
<li><a href="desc/addenda/LDC2018S14.and.flac">Android</a></li><br>
<li><a href="desc/addenda/LDC2018S14.ios.flac">iOS</a></li><br>
<li><a href="desc/addenda/LDC2018S14.txt">Transcript</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2018 Beijing Shell Shell Technology Co., Ltd., © 2018 Trustees of the University of Pennsylvania
DOI: 10.35111/kpzv-2f80
title: Avatar Education Portuguese

description: <h3>Introduction</h3><br>
<p>Avatar Education Portuguese was developed by the <a href="http://www.upe.br/">University of Pernambuco</a> and consists of approximately 80 minutes of Brazilian Portuguese microphone speech with phonetic and orthographic transcriptions. The data was developed for Avatar Education, an animated virtual assistant designed to enhance communication and interaction in educational contexts, such as online learning.</p><br>
<h3>Data</h3><br>
<p>The corpus contains 1,400 utterances (700 male and 700 female) of read and spontaneous speech spoken by two professional speakers. Utterances were transcribed at the word level (without time alignments) and at the phoneme level (with time alignment labels).</p><br>
<p>The audio data was recorded at 16kHz (mono, 16-bit) using <a href="https://www.avid.com/pro-tools-first/comparison">Pro Tools</a> recording software and stored in flac compressed wav format. The acoustic environment was controlled for background conditions that occur in application environments.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2018S15.flac">Audio</a></li><br>
<li><a href="desc/addenda/LDC2018S15.txt">Word Level Transcript</a></li><br>
<li><a href="desc/addenda/LDC2018S15.lab">Phoneme Level Transcript</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2018 Alexandre Magno Andrade Maciel, © 2018 Trustees of the University of Pennsylvania