cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35111/hm47-hd64
title: Multi-Language Conversational Telephone Speech 2011 -- Turkish

description: <h3>Introduction</h3><br>
<p>Multi-Language Conversational Telephone Speech 2011 -- Turkish was developed by the Linguistic Data Consortium (LDC) and is comprised of approximately 18 hours of telephone speech in Turkish.</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 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>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>Spanish (<a href="../../../LDC2018S12">LDC2018S12</a>)</li><br>
<li>Arabic (<a href="../../../LDC2019S02">LDC2019S02</a>)</li><br>
<li>English (<a href="../../../LDC2019S06">LDC2019S06</a>)</li><br>
</ul><br>
<p>Data</p><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>turkish</td><br>
<td>tur</td><br>
<td>87</td><br>
<td>18.6</td><br>
<td>975</td><br>
</tr><br>
</tbody><br>
</table><br>
<h3>Samples</h3><br>
<p>Please listen to this <a href="desc/addenda/LDC2017S09.flac">audio sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/cxwc-kb75
title: CHiME2 WSJ0

description: <h3>Introduction</h3><br>
<p>CHiME2 WSJ0 was developed as part of <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2013/">The 2nd CHiME Speech Separation and Recognition Challenge</a> and contains approximately 166 hours of English speech from a noisy living room environment. The CHiME Challenges focus on distant-microphone automatic speech recognition (ASR) in real-world environments.</p><br>
<p>CHiME2 WSJ0 reflects the <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2013/chime2_task2.html">medium vocabulary track</a> of the CHiME2 Challenge. 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>LDC also released CHiME2 Grid (<a href="../../../LDC2017S07">LDC2017S07</a>) and CHiME3 (<a href="../../../LDC2017S24">LDC2017S24</a>).</p><br>
<h3>Data</h3><br>
<p>Data is divided into training, development and test sets. All data is provided as 16 bit WAV files sampled at 16 kHz. The noisy utterances are in isolated form and in embedded form. The latter involves five seconds of background noise before and after the utterance. Seven hours of noise background not part of the training set are also included.</p><br>
<p>Also included are baseline scoring, decoding and retraining tools based on Cambridge University' s tool, <a href="http://htk.eng.cam.ac.uk/">HTK</a> (the Hidden Markov Toolkit) and related <a href="http://www.keithv.com/pub/baselinewsj/baseline_wsj_recipes.pdf">recipes</a>. These tools include three baseline speaker-independent recognition systems trained on clean, reverberated and noisy data, respectively, and a number of scripts.</p><br>
<h3>Samples</h3><br>
<p>Please listen to the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2017S10.embed.wav">Embedded</a></li><br>
<li><a href="desc/addenda/LDC2017S10.isolated.wav">Isolated</a></li><br>
<li><a href="desc/addenda/LDC2017S10.reverb.wav">Reverberated</a></li><br>
<li><a href="desc/addenda/LDC2017S10.scaled.wav">Scaled</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1987-1989 Dow Jones & Company, Inc., © 2017 Inria Nancy - Grand Est, University of Sheffield, Mitsubishi Electric Research Labs, Fondazione Bruno Kessler, © 1992, 1993, 1996, 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/cpnn-f916
title: Metalogue Multi-Issue Bargaining Dialogue

description: <h3>Introduction</h3><br>
<p>Metalogue Multi-Issue Bargaining Dialogue was developed by the <a href="http://www.metalogue.eu/consortium/">Metalogue Consortium</a> under the European Community's <a href="https://ec.europa.eu/research/fp7/index_en.cfm">Seventh Framework Programme for Research and Technological Development</a>. This release consists of approximately 2.5 hours of semantically annotated English dialogue data that includes speech and transcripts.</p><br>
<p>The goal of the Metalogue project was to develop a dialogue system with flexible dialogue management to enable the system's behavior in setting goals, choosing strategies and monitoring various processes. Participants were involved in a multi-issue bargaining scenario in which a representative of a city council and a representative of small business owners negotiated the implementation of new anti-smoking regulations. The negotiation involved four issues, each with four or five options. Participants received a preference profile for each scenario and negotiated for an agreement with the highest value based on their preference information. Negotiators were not allowed to accept an agreement with a negative value or to share their preference profiles with other participants.</p><br>
<h3>Data</h3><br>
<p>Six unique subjects (undergraduates between 19 and 25 years of age) participated in the collection. The dialogue speech was captured with two headset microphones and saved in 16kHz, 16-bit mono linear PCM FLAC format. Speech signal files are of two types: full dialogue session; and segmented speech signal, cut per speaker and roughly per turn.</p><br>
<p>Transcripts were produced semi-automatically, using an automatic speech recognizer followed by manual correction.</p><br>
<p>Seven types of annotation were performed manually using the <a href="http://www.anvil-software.org/">Anvil</a> tool: dialogue act annotations; discourse structure acts; contact management acts; task management dialogue acts; negotiation moves; rhetorical relations; and disfluencies in speech production. More information about the annotation process is included in the documentation.</p><br>
<p>All text is presented in UTF-8 as either plain text or XML.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2017S11.1.flac">Audio 1</a></li><br>
<li><a href="desc/addenda/LDC2017S11.1.txt">Transcript 1</a></li><br>
<li><a href="desc/addenda/LDC2017S11.2.flac">Audio 2</a></li><br>
<li><a href="desc/addenda/LDC2017S11.2.txt">Transcript 2</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 Saarland University (USAAR), © 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/q1eh-6457
title: KSUEmotions

description: <h3>Introduction</h3><br>
<p>KSUEmotions was developed by <a href="http://ksu.edu.sa/en/">King Saud University</a> (KSU) and contains approximately five hours of emotional Modern Standard Arabic (MSA) speech from 23 subjects. Speakers were from three countries: Yemen, Saudi Arabia and Syria.</p><br>
<p>Subjects read MSA sentences from newswire text in the following emotions: neutral, anger, sadness, happiness, surprise, and interrogative (asking a question). Human reviewers then listened to the recordings to identify the emotion they heard..</p><br>
<h3>Data</h3><br>
<p>Audio was recorded in each participant's home. Audio is presented as 16-bit 16 kHz flac compressed wav. In addition to speech files and metadata about the speakers, timeless label files and automatic time segmentation alignment files are included. Text is presented as UTF-8 plain text.</p><br>
<h3>Samples</h3><br>
<p>Please listen to this <a href="desc/addenda/LDC2017S12.flac">neutral audio sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 Ali Meftah, © 2017 Trustees of the University of Pennsylvania
DOI: 10.5066/p9h4m178
title: Drainage basins of selected streamgages in West Virginia through 2020

description: Drainage basin areas for 376 USGS streamgages in West Virginia and adjacent states were delineated digitally. The USGS Watershed Boundary Dataset HUC12 sub-watershed lines were used as outer limits of basins, and heads-up digitizing was used to delineate boundaries from the stream gage to the HUC12 boundary. The USGS National Map was used, as background, to show both contour lines and digital elevation to highlight drainage basin divides, ridges, and valleys. Basins were delineated for (1) all active continuous-flow and crest-stage streamgages, in West Virginia, through water year 2020, (2) selected inactive streamgages, in West Virginia and adjacent states, that were used in the flood-frequency report prepared by Wiley and Atkins (2010), (3) Selected streamflow stations in West Virginia included in the low-flow report by Wiley and Atkins (2006), 4) historic West Virginia streamgages with published daily flow values, and (5) selected stage-only streamgages.For some historical streamgages included in this dataset, either the basin perimeter or the stream location have been altered by human activity since the gage was operated. Drainage basins for these streamgages were digitized from topographic maps contemporaneous with the period of streamflow record, so that their published drainage areas would continue to accurately reflect the drainage area when streamflow data were collected. Streamgage 03198022 was digitized from the 1971 topographic map because the basin perimeter was altered by construction after the streamgage was discontinued, and streamgages 03212567 and 03213495 were digitized from 1968 topographic maps because the basin perimeter was altered by surface mining after the streamgage was discontinued. Streamgages 03055040 and 03059500 were operated on streams that have been moved substantially since the streamgages were discontinued; basins for these streamgages were digitized from the historic streamgage locations, which are no longer on streams. Six streamgages, 01607000, 01636451, 01636462, 03060000, 03181000, and 03183200 are included in the point file for completeness, although no basins were delineated for them because they were on springs or karst-affected streams where the surface drainage basin was known to have little relationship to the actual drainage basin. Drainage basin areas, provided in this data release were updated in the National Water Information System (NWIS). Provided in the file 'WVBasinboundary.zip' is a geodatabase with (1) a point feature class of streamgage stations as described above (WV_Surfacewater_sites), (2) a feature dataset that contains the digitized drainage areas in polygon(WVBoundary_poly) and line (WVboundary_arc) format.
DOI: 10.35111/6909-zp31
title: Vehicle City Voices Corpus – Part I

description: <h3>Introduction</h3><br>
<p>Vehicle City Voices Corpus &ndash; Part I was developed at the <a href="https://www.umflint.edu/">University of Michigan-Flint</a>, and is an ongoing oral history project and survey of English language variation in Flint, Michigan. It contains approximately 16 hours of speech with corresponding transcripts from interviews of Flint residents conducted between 2012 and 2015.</p><br>
<p>The corpus was designed to provide high-quality recordings for acoustic analysis and to examine narrative structure and discursive construction of individual and collective identity in urban spaces.</p><br>
<h3>Data</h3><br>
<p>This release is comprised of 21 interviews by undergraduate and graduate students for civic engagement projects in linguistics courses and by a graduate student research assistant. Participants (11 female, 10 male) were born between 1935 and 1991 and represented a range of ages, genders, and ethnicities. Of the interviewees, 11 were Black/African American, 8 were White/Caucasian, and 2 were biracial/mixed ethnic heritage.</p><br>
<p>Interviews took place in various locations in Flint, including university and community spaces and a church meeting room. Questions focused on recollections of important community events, remembrances about the community, the interviewee's relationship to the auto industry and the city's physical transformation, among other topics.</p><br>
<p>Sessions were recorded using Marantz PMD661 portable SD recorders with accompanying Audio-Technica AT831B lavalier condenser microphones. The original recordings were uncompressed (PCM-16) sound files stored in WAV format recorded at a sampling rate of 44,100 Hz. These files were then converted to FLAC format. Transcripts are plain text and UTF-8.</p><br>
<p>Metadata (where provided by participants) includes information on gender, ethnicity, year of birth, level of education, field of employment, average income, length of time living in Flint and its surrounding areas, as well as interviewer age, gender, and ethnicity. In addition, original interview durations, edited interview durations, interview year, and transcript word counts are also provided in the metadata file.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2017S17.flac">audio sample</a> and <a href="desc/addenda/LDC2017S17.txt">transcript sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 The Regents of the University of Michigan, © 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/aw84-h425
title: SRI-FRTIV

description: <h3>Introduction</h3><br>
<p>SRI-FRTIV (Five-way Recorded Toastmaster Intrinsic Variation) was developed by <a href="https://www.sri.com/">SRI International</a> in 2007-2008 and is comprised of approximately 232 hours of English speech from thirty-four speakers who were members of <a href="https://www.toastmasters.org/">Toastmaster</a> clubs. Participants were asked to speak at three different levels of effort (low, normal and high) in four different styles (interview, conversation, reading and oration) to study the question of how intrinsic variations -- associated with the speaker rather than the recording environment -- affect text-independent speaker verification.</p><br>
<h3>Data</h3><br>
<p>Participants were native speakers of North American English who were members of local Toastmasters clubs and had experience in public speaking. This release includes demographic information for 30 speakers (15 male, 15 female), including gender, birth year, height, education level, years in Toastmasters, and a self-evaluation of speaking skills.</p><br>
<p>Not all effort levels were applicable for each speaking style and so were not collected. Interviews and phone conversations were not recorded at high effort and oration was not recorded at low or normal effort levels.</p><br>
<p>Speech data is presented as 16kHz 16-bit single channel flac compressed pcm wav (.flac).</p><br>
<h3>Samples</h3><br>
<p>Please listen to this <a href="desc/addenda/LDC2017S18.flac">low effort interview sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2017 SRI International, © 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/5893-bd53
title: ASpIRE Development and Development Test Sets

description: <h3>Introduction</h3><br>
<p>ASpIRE Development and Development Test Sets was developed for the Automatic Speech recognition In Reverberant Environments (ASpIRE) <a href="https://www.iarpa.gov/index.php/working-with-iarpa/prize-challenges/306-automatic-speech-in-reverberant-environments-aspire-challenge">Challenge</a> sponsored by IARPA (the Intelligent Advanced Research Projects Activity). It contains approximately 226 hours of English speech with transcripts and scoring files.</p><br>
<p>The ASpIRE challenge asked solvers to develop innovative speech recognition systems that could be trained on conversational telephone speech, and yet work well on far-field microphone data from noisy, reverberant rooms. Participants had the opportunity to evaluate their techniques on a common set of challenging data that included significant room noise and reverberation.</p><br>
<h3>Data</h3><br>
<p>The audio data is a subset of Mixer 6 Speech (<a href="../../../LDC2013S03">LDC2013S03</a>), audio recordings of interviews, transcript readings and conversational telephone speech collected by the Linguistic Data Consortium in 2009 and 2010 from native English speakers local to the Philadelphia area. The transcripts were developed by <a href="http://www.appen.com/">Appen</a> for the ASpIRE challenge.</p><br>
<p>Data is divided into development and development test sets.</p><br>
<p>Audio is presented as single channel, 16kHz 16-bit Signed Integer PCM *.wav files. Transcripts are plain text tdf files. Scoring files are also included.</p><br>
<h3>Samples</h3><br>
<p>Please view this <a href="desc/addenda/LDC2017S21.wav">audio sample</a> and <a href="desc/addenda/LDC2017S21.txt">transcript sample</a>.</p><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2014 U.S. Government, © 2009-2010, 2013, 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/v154-hj21
title: CHiME3

description: <h3>Introduction</h3><br>
<p>CHiME3 was developed as part of <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2015/">The 3rd CHiME Speech Separation and Recognition Challenge</a> and contains approximately 342 hours of English speech and transcripts from noisy environments and 50 hours of noisy environment audio. The CHiME Challenges focus on distant-microphone automatic speech recognition (ASR) in real-world environments. See the <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2015/index.html">CHIME3 home page</a> for more information.</p><br>
<p>The task in CHiME3 was similar to the <a href="http://spandh.dcs.shef.ac.uk/chime_challenge/chime2013/chime2_task2.html"> medium vocabulary track</a> of the CHiME2 Challenge in that 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. CHiME3 involved two types of data: speech data recorded in very noisy environments (on a bus, in a cafe, pedestrian area, and street junction) and noisy utterances generated by artificially mixing clean speech data with noisy backgrounds.</p><br>
<p>LDC has also released two CHiME2 corpora -- CHiME2 Grid (<a href="../../../LDC2017S07">LDC2017S07</a>) and CHiME2 WSJ0 (<a href="../../../LDC2017S10">LDC2017S10</a>).</p><br>
<h3>Data</h3><br>
<p>Data is divided into training, development and test sets. All data is provided as 16 bit WAV files sampled at 16 kHz. The audio data consists of the background noises, enhanced speech data using the baseline speech enhancement technique, unsegmented noisy speech data, and segmented noisy speech data.</p><br>
<p>Annotation files are based on JSON (<a href="http://json.org/">JavaScript Object Notation</a>) format. Transcripts are plain text in either DOT or TRN format. Also included are three software tools for acoustic simulation, speech enhancement, and ASR.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2017S24.iso.wav">Isolated</a></li><br>
<li><a href="desc/addenda/LDC2017S24.enh.wav">Enhanced</a></li><br>
<li><a href="desc/addenda/LDC2017S24.emb.wav">Embedded</a></li><br>
<li><a href="desc/addenda/LDC2017S24.bac.wav">Background</a></li><br>
<li><a href="desc/addenda/LDC2017S24.txt">Transcript</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 1987-1989 Dow Jones & Company, Inc., © 2017 Inria Nancy - Grand Est, University of Sheffield, Mitsubishi Electric Research Labs, Fondazione Bruno Kessler, © 1992, 1993, 1996, 2017 Trustees of the University of Pennsylvania
DOI: 10.35111/2zpz-q591
title: MWE-Aware English Dependency Corpus

description: <h3>Introduction</h3><br>
<p>MWE-Aware English Dependency Corpus 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 function 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 is available from LDC as MWE-Aware English Dependency Corpus 2.0 (<a href="../../../LDC2017T16">LDC2017T16</a>)</p><br>
<h3>Data</h3><br>
<p>MWE-Aware English Dependency Corpus 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/LDC2017T01.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