Difference between revisions of "Datasets"
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== [[Automatic speech recognition]] == | == [[Automatic speech recognition]] == | ||
| − | '''CHiME Challenge (2011)''' | + | '''1st CHiME Challenge (2011)''' |
Artificially distorted version of the small vocabulary [http://spandh.dcs.shef.ac.uk/gridcorpus/ GRID audio-visual corpus] (audio only). Binaural reverberated speech with speaker situated in front of the microphones. Additive household noises impinging from different directions. Clean-training, noisy-training, development and evaluation sets available, see | Artificially distorted version of the small vocabulary [http://spandh.dcs.shef.ac.uk/gridcorpus/ GRID audio-visual corpus] (audio only). Binaural reverberated speech with speaker situated in front of the microphones. Additive household noises impinging from different directions. Clean-training, noisy-training, development and evaluation sets available, see | ||
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''Resources'' | ''Resources'' | ||
| − | *Training recipe for HTK [http://spandh.dcs.shef.ac.uk/projects/chime/PCC/evaluation.html here]. | + | *Training recipe of the challenge for [[Software#Automatic_speech_recognition|HTK]] [http://spandh.dcs.shef.ac.uk/projects/chime/PCC/evaluation.html here]. |
''Baselines'' | ''Baselines'' | ||
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* See the paper above for results on a wide range of techniques | * See the paper above for results on a wide range of techniques | ||
| + | '''AURORA 5 (2007)''' | ||
| + | |||
| + | Artificially distorted version of the digits [http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S10 TI-DIGITS corpus]. Additive noise and additive noise plus reverberant speech sets. Variable SNR range. | ||
| + | |||
| + | Corpora available from LDC [http://catalog.elra.info/product_info.php?cPath=37_40&products_id=1015 here] | ||
'''AURORA 4 (2002)''' | '''AURORA 4 (2002)''' | ||
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''Resources'' | ''Resources'' | ||
| − | *Training recipe for HTK available [http://www.keithv.com/software/htk/ here]. Note that this recipe is for Wall-Street Journal (WSJ0), which is the clean speech version of AURORA4. Small changes are needed in the feature extraction scripts to account for different file terminations. | + | *Training recipe for [[Software#Automatic_speech_recognition|HTK]] available [http://www.keithv.com/software/htk/ here]. Note that this recipe is for Wall-Street Journal (WSJ0), which is the clean speech version of AURORA4. Small changes are needed in the feature extraction scripts to account for different file terminations. |
''Baselines'' | ''Baselines'' | ||
Revision as of 21:49, 25 August 2013
This page provides a number of datasets grouped by application, as well as research results (papers, numerical results, output transcriptions, intermediary data, etc) corresponding to each dataset.
Contents
Automatic speech recognition
1st CHiME Challenge (2011)
Artificially distorted version of the small vocabulary GRID audio-visual corpus (audio only). Binaural reverberated speech with speaker situated in front of the microphones. Additive household noises impinging from different directions. Clean-training, noisy-training, development and evaluation sets available, see
- Jon Barker, E. Vincent, N. Ma, H. Christensen, P. Green, "The PASCAL CHiME speech separation and recognition challenge", Computer Speech & Language, Volume 27, Issue 3, May 2013, Pages 621-633.
Available from Computer Speech and Language here
Corpus available here (no cost)
Resources
Baselines
- See the paper above for results on a wide range of techniques
AURORA 5 (2007)
Artificially distorted version of the digits TI-DIGITS corpus. Additive noise and additive noise plus reverberant speech sets. Variable SNR range.
Corpora available from LDC here
AURORA 4 (2002)
Artificially distorted version of the 5K word Wall Street Journal corpus (WSJ0). Stationary and non-stationary noises added. Second recordings with distant mismatched microphone. Clean-training, mixed-training, noisy training and test sets available. No evaluation set, see
- Günter Hirsch "Experimental Framework for the Performance Evaluation of Speech Recognition Front-ends on a Large Vocabulary Task", ETSI STQ Aurora DSR Working Group, 2002.
Corpora available from ELRA here and here
Resources
- Training recipe for HTK available here. Note that this recipe is for Wall-Street Journal (WSJ0), which is the clean speech version of AURORA4. Small changes are needed in the feature extraction scripts to account for different file terminations.
Baselines
Speaker identification and verification
Speech enhancement and separation
Other applications
Contribute a dataset
To contribute a new dataset, please
- create an account and login
- go to the wiki page above corresponding to your application; if it does not exist yet, you may create it
- click on the "Edit" link at the top of the page and add a new section for your dataset (the datasets are ordered by year of collection)
- click on the "Save page" link at the bottom of the page to save your modifications
Please make sure to provide the following information:
- name of the dataset and year of collection
- authors, institution, contact information
- link to the dataset and to side resources (lexicon, language model, etc)
- short description (nature of the data, license, etc) and link to a paper/report describing the dataset, if any
- at least 1 research result obtained for this dataset (see below)
We currently cannot provide storage space for large datasets. Please upload the dataset at a stable URL on the website of your institution or elsewhere and provide its URL only. If this is not possible, please contact the resources sharing working group.
Contribute a research result
To contribute a new research result, please
- create an account and login
- go to the wiki page and the section corresponding to the dataset for which this result was obtained
- click on the "Edit" link on the right of the section header and add a new item for your result
- click on the "Save page" link at the bottom of the page to save your modifications
Please make sure to provide the following information:
- authors, paper/report title, means of publication
- link to the pdf of the paper
- link to derived data (output transcriptions, intermediary data, etc)
In order to save storage space, please do not upload the paper on this wiki, but link it as much as possible from your instutitutional archive, from another public archive (e.g., arxiv) or from the publisher website (e.g., ieexplore).
We currently cannot provide storage space for large datasets. Please upload the derived data at a stable URL on the website of your institution or elsewhere and provide its URL only. If this is not possible, please contact the resources sharing working group.
