oai:www.ldc.upenn.edu:LDC2017S10
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OLAC Record oai:www.ldc.upenn.edu:LDC2017S10 |
| Metadata | ||
| Title: | CHiME2 WSJ0 | |
| Access Rights: | Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining | |
| Bibliographic Citation: | Vincent, Emmanuel, et al. CHiME2 WSJ0 LDC2017S10. Web Download. Philadelphia: Linguistic Data Consortium, 2017 | |
| Contributor: | Vincent, Emmanuel | |
| Barker, Jon | ||
| Watanabe, Shinji | ||
| Le Roux, Jonathan | ||
| Nesta, Francesco | ||
| Matassoni, Marco | ||
| Date (W3CDTF): | 2017 | |
| Date Issued (W3CDTF): | 2017-06-15 | |
| Description: | *Introduction* CHiME2 WSJ0 was developed as part of The 2nd CHiME Speech Separation and Recognition Challenge 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. CHiME2 WSJ0 reflects the medium vocabulary track of the CHiME2 Challenge. The target utterances were taken from CSR-I (WSJ0) Complete (LDC93S6A), specifically, the 5,000 word subset of read speech from Wall Street Journal news text. LDC also released CHiME2 Grid (LDC2017S07) and CHiME3 (LDC2017S24). *Data* 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. Also included are baseline scoring, decoding and retraining tools based on Cambridge University' s tool, HTK (the Hidden Markov Toolkit) and related recipes. These tools include three baseline speaker-independent recognition systems trained on clean, reverberated and noisy data, respectively, and a number of scripts. *Samples* Please listen to the following samples: * Embedded * Isolated * Reverberated * Scaled *Updates* None at this time. | |
| Extent: | Corpus size: 38385568 KB | |
| Format: | Sampling Rate: 16000 | |
| Sampling Format: pcm | ||
| Identifier: | LDC2017S10 | |
| https://catalog.ldc.upenn.edu/LDC2017S10 | ||
| ISBN: 1-58563-801-3 | ||
| ISLRN: 071-714-384-459-0 | ||
| DOI: 10.35111/cxwc-kb75 | ||
| Language: | English | |
| Language (ISO639): | eng | |
| License: | LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf | |
| Medium: | Distribution: Web Download | |
| Publisher: | Linguistic Data Consortium | |
| Publisher (URI): | https://www.ldc.upenn.edu | |
| Relation (URI): | https://catalog.ldc.upenn.edu/docs/LDC2017S10 | |
| Rights Holder: | 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 | |
| Type (DCMI): | Sound | |
| Type (OLAC): | primary_text | |
OLAC Info |
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| Archive: | The LDC Corpus Catalog | |
| Description: | http://www.language-archives.org/archive/www.ldc.upenn.edu | |
| GetRecord: | OAI-PMH request for OLAC format | |
| GetRecord: | Pre-generated XML file | |
OAI Info |
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| OaiIdentifier: | oai:www.ldc.upenn.edu:LDC2017S10 | |
| DateStamp: | 2020-11-30 | |
| GetRecord: | OAI-PMH request for simple DC format | |
Search Info | ||
| Citation: | Vincent, Emmanuel; Barker, Jon; Watanabe, Shinji; Le Roux, Jonathan; Nesta, Francesco; Matassoni, Marco. 2017. Linguistic Data Consortium. | |
| Terms: | area_Europe country_GB dcmi_Sound iso639_eng olac_primary_text | |