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ISCApad #308 |
| Saturday, February 10, 2024 by Chris Wellekens |
5-2 Database
| 5-2-1 | Linguistic Data Consortium (LDC) update (January 2024) In this newsletter: Renew your LDC membership today New publications: * LORELEI Farsi Representative Language Pack was developed by LDC and is comprised of approximately 250 million words of Farsi monolingual text, 120,000 Farsi words translated from English data, and 751,000 words of found Farsi-English parallel text. Approximately 75,000 words were annotated for named entities and up to 22,000 words were annotated for entity discovery and linking and situation frames (identifying entities, needs, and issues). Data was collected from discussion forum, news, reference, social network, and weblogs.
Membership Coordinator University of Pennsylvania T: +1-215-573-1275 E: ldc@ldc.upenn.edu M: 3600 Market St. Suite 810 Philadelphia, PA 19104
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| 5-2-2 | ELRA - Language Resources Catalogue - Update (November and December2023) We are happy to announce that 1 new written corpus, 1 new monolingual lexicon and 2 new speech resources are now available in our catalogue. Archives of ELRA Language Resources Catalogue Updates *************************************************************** We are happy to announce that 3 new monolingual lexicons are now available in our catalogue.
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| 5-2-3 | Speechocean – update (August 2019)
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| 5-2-4 | Google 's Language Model benchmark A LM benchmark is available at:https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark
Here is a brief description of the project.
'The purpose of the project is to make available a standard training and test setup for language modeling experiments. The training/held-out data was produced from a download at statmt.org using a combination of Bash shell and Perl scripts distributed here. This also means that your results on this data set are reproducible by the research community at large. Besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the following baseline models:
ArXiv paper: http://arxiv.org/abs/1312.3005
Happy benchmarking!'
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| 5-2-5 | Forensic database of voice recordings of 500+ Australian English speakers Forensic database of voice recordings of 500+ Australian English speakers
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| 5-2-6 | Audio and Electroglottographic speech recordings
Audio and Electroglottographic speech recordings from several languages We are happy to announce the public availability of speech recordings made as part of the UCLA project 'Production and Perception of Linguistic Voice Quality'. http://www.phonetics.ucla.edu/voiceproject/voice.html Audio and EGG recordings are available for Bo, Gujarati, Hmong, Mandarin, Black Miao, Southern Yi, Santiago Matatlan/ San Juan Guelavia Zapotec; audio recordings (no EGG) are available for English and Mandarin. Recordings of Jalapa Mazatec extracted from the UCLA Phonetic Archive are also posted. All recordings are accompanied by explanatory notes and wordlists, and most are accompanied by Praat textgrids that locate target segments of interest to our project. Analysis software developed as part of the project – VoiceSauce for audio analysis and EggWorks for EGG analysis – and all project publications are also available from this site. All preliminary analyses of the recordings using these tools (i.e. acoustic and EGG parameter values extracted from the recordings) are posted on the site in large data spreadsheets. All of these materials are made freely available under a Creative Commons Attribution-NonCommercial-ShareAlike-3.0 Unported License. This project was funded by NSF grant BCS-0720304 to Pat Keating, Abeer Alwan and Jody Kreiman of UCLA, and Christina Esposito of Macalester College. Pat Keating (UCLA)
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| 5-2-7 | EEG-face tracking- audio 24 GB data set Kara One, Toronto, Canada We are making 24 GB of a new dataset, called Kara One, freely available. This database combines 3 modalities (EEG, face tracking, and audio) during imagined and articulated speech using phonologically-relevant phonemic and single-word prompts. It is the result of a collaboration between the Toronto Rehabilitation Institute (in the University Health Network) and the Department of Computer Science at the University of Toronto.
In the associated paper (abstract below), we show how to accurately classify imagined phonological categories solely from EEG data. Specifically, we obtain up to 90% accuracy in classifying imagined consonants from imagined vowels and up to 95% accuracy in classifying stimulus from active imagination states using advanced deep-belief networks.
Data from 14 participants are available here: http://www.cs.toronto.edu/~complingweb/data/karaOne/karaOne.html.
If you have any questions, please contact Frank Rudzicz at frank@cs.toronto.edu.
Best regards, Frank
PAPER Shunan Zhao and Frank Rudzicz (2015) Classifying phonological categories in imagined and articulated speech. In Proceedings of ICASSP 2015, Brisbane Australia ABSTRACT This paper presents a new dataset combining 3 modalities (EEG, facial, and audio) during imagined and vocalized phonemic and single-word prompts. We pre-process the EEG data, compute features for all 3 modalities, and perform binary classi?cation of phonological categories using a combination of these modalities. For example, a deep-belief network obtains accuracies over 90% on identifying consonants, which is signi?cantly more accurate than two baseline supportvectormachines. Wealsoclassifybetweenthedifferent states (resting, stimuli, active thinking) of the recording, achievingaccuraciesof95%. Thesedatamaybeusedtolearn multimodal relationships, and to develop silent-speech and brain-computer interfaces.
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| 5-2-8 | TORGO data base free for academic use. In the spirit of the season, I would like to announce the immediate availability of the TORGO database free, in perpetuity for academic use. This database combines acoustics and electromagnetic articulography from 8 individuals with speech disorders and 7 without, and totals over 18 GB. These data can be used for multimodal models (e.g., for acoustic-articulatory inversion), models of pathology, and augmented speech recognition, for example. More information (and the database itself) can be found here: http://www.cs.toronto.edu/~complingweb/data/TORGO/torgo.html.
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| 5-2-9 | Datatang Datatang is a global leading data provider that specialized in data customized solution, focusing in variety speech, image, and text data collection, annotation, crowdsourcing services.
Summary of the new datasets (2018) and a brief plan for 2019.
? Speech data (with annotation) that we finished in 2018
?2019 ongoing speech project
On top of the above, there are more planed speech data collections, such as Japanese speech data, children`s speech data, dialect speech data and so on.
What is more, we will continually provide those data at a competitive price with a maintained high accuracy rate.
If you have any questions or need more details, do not hesitate to contact us jessy@datatang.com
It would be possible to send you with a sample or specification of the data.
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| 5-2-10 | SIWIS French Speech Synthesis Database The SIWIS French Speech Synthesis Database includes high quality French speech recordings and associated text files, aimed at building TTS systems, investigate multiple styles, and emphasis. A total of 9750 utterances from various sources such as parliament debates and novels were uttered by a professional French voice talent. A subset of the database contains emphasised words in many different contexts. The database includes more than ten hours of speech data and is freely available.
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| 5-2-11 | JLCorpus - Emotional Speech corpus with primary and secondary emotions JLCorpus - Emotional Speech corpus with primary and secondary emotions:
For further understanding the wide array of emotions embedded in human speech, we are introducing an emotional speech corpus. In contrast to the existing speech corpora, this corpus was constructed by maintaining an equal distribution of 4 long vowels in New Zealand English. This balance is to facilitate emotion related formant and glottal source feature comparison studies. Also, the corpus has 5 secondary emotions along with 5 primary emotions. Secondary emotions are important in Human-Robot Interaction (HRI), where the aim is to model natural conversations among humans and robots. But there are very few existing speech resources to study these emotions,and this work adds a speech corpus containing some secondary emotions. Please use the corpus for emotional speech related studies. When you use it please include the citation as: Jesin James, Li Tian, Catherine Watson, 'An Open Source Emotional Speech Corpus for Human Robot Interaction Applications', in Proc. Interspeech, 2018. To access the whole corpus including the recording supporting files, click the following link: https://www.kaggle.com/tli725/jl-corpus, (if you have already installed the Kaggle API, you can type the following command to download: kaggle datasets download -d tli725/jl-corpus) Or if you simply want the raw audio+txt files, click the following link: https://www.kaggle.com/tli725/jl-corpus/downloads/Raw%20JL%20corpus%20(unchecked%20and%20unannotated).rar/4 The corpus was evaluated by a large scale human perception test with 120 participants. The link to the survey are here- For Primary emorion corpus: https://auckland.au1.qualtrics.com/jfe/form/SV_8ewmOCgOFCHpAj3 For Secondary emotion corpus: https://auckland.au1.qualtrics.com/jfe/form/SV_eVDINp8WkKpsPsh These surveys will give an overall idea about the type of recordings in the corpus. The perceptually verified and annotated JL corpus will be given public access soon.
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| 5-2-12 | OPENGLOT –An open environment for the evaluation of glottal inverse filtering OPENGLOT –An open environment for the evaluation of glottal inverse filtering
OPENGLOT is a publically available database that was designed primarily for the evaluation of glottal inverse filtering algorithms. In addition, the database can be used in evaluating formant estimation methods. OPENGLOT consists of four repositories. Repository I contains synthetic glottal flow waveforms, and speech signals generated by using the Liljencrants–Fant (LF) waveform as an excitation, and an all-pole vocal tract model. Repository II contains glottal flow and speech pressure signals generated using physical modelling of human speech production. Repository III contains pairs of glottal excitation and speech pressure signal generated by exciting 3D printed plastic vocal tract replica with LF excitations via a loudspeaker. Finally, Repository IV contains multichannel recordings (speech pressure signal, EGG, high-speed video of the vocal folds) from natural production of speech.
OPENGLOT is available at: http://research.spa.aalto.fi/projects/openglot/
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| 5-2-13 | Corpus Rhapsodie Nous sommes heureux de vous annoncer la publication d¹un ouvrage consacré
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| 5-2-14 | The My Science Tutor Children?s Conversational Speech Corpus (MyST Corpus) , Boulder Learning Inc. The My Science Tutor Children?s Conversational Speech Corpus (MyST Corpus) is the world?s largest English children?s speech corpus. It is freely available to the research community for research use. Companies can acquire the corpus for $10,000. The MyST Corpus was collected over a 10-year period, with support from over $9 million in grants from the US National Science Foundation and Department of Education, awarded to Boulder Learning Inc. (Wayne Ward, Principal Investigator). The MyST corpus contains speech collected from 1,374 third, fourth and fifth grade students. The students engaged in spoken dialogs with a virtual science tutor in 8 areas of science. A total of 11,398 student sessions of 15 to 20 minutes produced a total of 244,069 utterances. 42% of the utterances have been transcribed at the word level. The corpus is partitioned into training and test sets to support comparison of research results across labs. All parents and students signed consent forms, approved by the University of Colorado?s Institutional Review Board, that authorize distribution of the corpus for research and commercial use. The MyST children?s speech corpus contains approximately ten times as many spoken utterances as all other English children?s speech corpora combined (see https://en.wikipedia.org/wiki/List_of_children%27s_speech_corpora). Additional information about the corpus, and instructions for how to acquire the corpus (and samples of the speech data) can be found on the Boulder Learning Web site at http://boulderlearning.com/request-the-myst-corpus/.
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| 5-2-15 | HARVARD speech corpus - native British English speaker
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| 5-2-16 | Magic Data Technology Kid Voice TTS Corpus in Mandarin Chinese (November 2019) Magic Data Technology Kid Voice TTS Corpus in Mandarin Chinese
Magic Data Technology is one of the leading artificial intelligence data service providers in the world. The company is committed to providing a wild range of customized data services in the fields of speech recognition, intelligent imaging and Natural Language Understanding.
This corpus was recorded by a four-year-old Chinese girl originally born in Beijing China. This time we published 15-minute speech data from the corpus for non-commercial use.
The contents and the corresponding descriptions of the corpus:
The corpus aims to help researchers in the TTS fields. And it is part of a much bigger dataset (2.3 hours MAGICDATA Kid Voice TTS Corpus in Mandarin Chinese) which was recorded in the same environment. This is the first time to publish this voice!
Please note that this corpus has got the speaker and her parents’ authorization.
Samples are available. Do not hesitate to contact us for any questions. Website: http://www.imagicdatatech.com/index.php/home/dataopensource/data_info/id/360 E-mail: business@magicdatatech.com
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| 5-2-17 | FlauBERT: a French LM Here is FlauBERT: a French LM learnt (with #CNRS J-Zay supercomputer) on a large and heterogeneous corpus. Along with it comes FLUE (evaluation setup for French NLP). FlauBERT was successfully applied to complex tasks (NLI, WSD, Parsing). More on https://github.com/getalp/Flaubert
More details on this online paper: https://arxiv.org/abs/1912.05372
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| 5-2-18 | ELRA-S0408 SpeechTera Pronunciation Dictionary ELRA-S0408 Speechtera Pronunciation Dictionary ISLRN: 645-563-102-594-8
The SpeechTera Pronunciation Dictionary is a machine-readable pronunciation dictionary for Brazilian Portuguese and comprises 737,347 entries. Its phonetic transcription is based on 13 linguistics varieties spoken in Brazil and contains the pronunciation of the frequent word forms found in the transcription data of the SpeechTera's speech and text database (literary, newspaper, movies, miscellaneous). Each one of the thirteen dialects comprises 56,719 entries. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-S0408/ For more information on the catalogue, please contact Valérie Mapelli mailto:mapelli@elda.org If you would like to enquire about having your resources distributed by ELRA, please do not hesitate to contact us. Visit our On-line Catalogue: http://catalog.elra.info
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| 5-2-19 | Ressources of ELRC Network Paris, France, April 23, 2020 ELRA is happy to announce that Language Resources collected within the ELRC Network, funded by the European Commission, are now available from the ELRA Catalogue of Language Resources. For more information on the catalogue, please contact Valérie Mapelli
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| 5-2-20 | ELRA announces that MEDIA data are now available for free for academic research ELRA announces that MEDIA data are now available for free for academic research Further to the request of the HLT French community to foster evaluation activities for man-machine dialogue systems for French language, ELRA has decided to provide a free access to the MEDIA speech corpora and evaluation package for academic research purposes. The MEDIA data can be found in the ELRA Catalogue under the following references: Data available from the ELRA Catalogue can be obtained easily by contacting ELRA. The MEDIA project was carried out within the framework of Technolangue, the French national research programme funded by the French Ministry of Research and New Technologies (MRNT) with the objective of running a campaign for the evaluation of man-machine dialogue systems for French. The campaign was distributed over two actions: an evaluation taking into account the dialogue context and an evaluation not taking into account the dialogue context. PortMedia was a follow up project supported by the French Research Agency (ANR). The French and Italian corpus was produced by ELDA, with the same paradigm and specifications as the MEDIA speech database but on a different domain. For more information and/or questions, please write to contact@elda.org. *** About ELRA *** To find out more about ELRA and its respective catalogue, please visit: http://www.elra.info and http://catalogue.elra.info
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| 5-2-21 | ELRA/ELDA Communication : LT4all Out of the 7,000+ language spoken around the world, only a few have associated Language Technologies. The majority of languages can be considered as 'under-resourced' or as 'not supported'. This situation, very detrimental to many languages speakers, and specifically indigenous languages speakers, creates a digital divide, and places many languages in danger of digital extinction, if not complete extinction.
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| 5-2-22 | Search and Find ELRA LRs on Google Dataset Search and ELG LT Platform Search and Find ELRA LRs on Google Dataset Search and ELG LT Platform ELRA is happy to announce that all the Language Resources from its Catalogue can now be searched and found on Google Dataset Search and on the ELG Language Technology platform developed within the European Language Grid project. In order to allow the indexing by Google Dataset Search, ELRA has updated the code generating the catalogue pages. The code developed follows the schema.org standard and is publicly available in JSON format so that it can be used for other harvesting purposes. The ELRA Catalogue is already indexed and harvested by famous repositories and archives such as OLAC (Open Language Archives Community), CLARIN Virtual Language Observatory and META-SHARE. For 25 years now, ELRA has been distributing Language Resources to support research and development in various fields of Human Language Technology. The indexing on both Google Dataset Search and the ELG LT Plaform is increasing ELRA Catalogue?s visibility, making the LRs known to new visitors from the Human Language Technologies, Artificial Intelligence and other related fields. *** About ELRA *** The European Language Resources Association (ELRA) is a non-profit making organisation founded by the European Commission in 1995, with the mission of providing a clearing house for language resources and promoting Human Language Technologies (HLT). ELRA Catalogue of Language Resources: http://catalogue.elra.info More about ELRA, please visit: http://www.elra.info. *** About Google Dataset Search *** Google Dataset Search is a search engine for datasets that enables users to search through a list of data repositories indexed through a standardised schema. More about Google Dataset Search: https://datasetsearch.research.google.com/ *** About European Language Grid *** The European Language Grid (ELG) is a project funded by the European Union through the Horizon 2020 research and innovation programme. It aims to be a primary platform for Language Technology in Europe. More about the European Language Grid project: https://www.european-language-grid.eu/
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| 5-2-23 | Sharing language resources (ELRA) ELRA recognises the importance of sharing Language Resources (LRs) and making them available to the community. Since the 2014 edition of LREC, the Language Resources and Evaluation Conference, participants have been offered the possibility to share their LRs (data, tools, web-services, etc.) when submitting a paper, uploading them in a special LREC repository set up by ELRA. This effort of sharing LRs, linked to the LRE Map initiative (https://lremap.elra.info) for their description, contributes to creating a common repository where everyone can deposit and share data. Despite the cancellation of LREC 2020 in Marseille, a high number of contributions was submitted and the LREC initiative 'Share your LRs' could be conducted to the end successfully. Repositories corresponding to each edition of the conference are available here:
For more info and questions, please write to contact@elda.org.
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| 5-2-24 | The UCLA Variability Speaker Database With NSF support, our interdisciplinary team of voice research at UCLA recently put together a public database that we believe will be of interest to many members of the ISCA community. On behalf of my co-authors (Patricia Keating, Jody Kreiman, Abeer Alwan, Adam Chong), I'm writing to ask if we could advertise our database in the ISCA newsletter. We'd really appreciate your help with this. The database, the UCLA Variability Speaker Database, is freely available through UCLA's Box cloud, which can be accessed from our lab website: http://www.seas.ucla.edu/spapl/shareware.html#Data I should mention that the database will also be available from the Linguistic Data Consortium (LDC) as of October, 2021.
Here's a brief description of the database.
Postdoctoral Scholar, Department of Linguistics, UCLA
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| 5-2-25 | Free databases in Catalan, Spanish and Arabic (ELRA and UPC Spain) We are pleased to announce that Language Resources entrusted to ELRA for distribution and shared by the Universitat Politecnica de Catalunya (UPC), in Spain, are now available for free for academic research purposes (for ELRA institutional members) and at substantially decreased costs for commercial purposes. All data have been developed to enhance Speech technologies in Catalan, Spanish and Arabic. The Language Resources can be found in the ELRA Catalogue under the following references: ELRA-S0101 Spanish SpeechDat(II) FDB-1000 (ISLRN: 415-072-153-167-5)
For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0101/ ELRA-S0102 Spanish SpeechDat(II) FDB-4000 (ISLRN: 295-399-069-106-4) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0102/ ELRA-S0140 Spanish SpeechDat-Car database (ISLRN: 937-459-364-430-3) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0140/ ELRA-S0141 SALA Spanish Venezuelan Database (ISLRN: 894-744-522-508-8) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0141/ ELRA-S0173 SALA Spanish Mexican Database (ISLRN: 077-043-759-782-3) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0173/ ELRA-S0183 OrienTel Morocco MCA (Modern Colloquial Arabic) database (ISLRN: 613-578-868-832-2) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0183/ ELRA-S0184 OrienTel Morocco MSA (Modern Standard Arabic) database (ISLRN: 978-839-138-181-8) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0184/ ELRA-S0185 OrienTel French as spoken in Morocco database (ISLRN: 299-422-451-969-8) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0185/ ELRA-S0186 OrienTel Tunisia MCA (Modern Colloquial Arabic) database (ISLRN: 297-705-745-294-4) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0186/ ELRA-S0187 OrienTel Tunisia MSA (Modern Standard Arabic) database (ISLRN: 926-401-827-806-5) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0187/ ELRA-S0188 OrienTel French as spoken in Tunisia database (ISLRN: 085-972-271-578-3) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0188/ ELRA-S0207 LC-STAR Catalan phonetic lexicon (ISLRN: 102-856-174-704-7) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0207/ ELRA-S0208 LC-STAR Spanish phonetic lexicon (ISLRN: 826-939-678-247-5) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0208/ ELRA-S0243 SpeechDat Catalan FDB database (ISLRN: 373-541-490-506-3) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0243/ ELRA-S0306 TC-STAR Transcriptions of Spanish Parliamentary Speech (ISLRN: 972-398-693-247-4 ) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0306/ ELRA-S0309 TC-STAR Spanish Baseline Female Speech Database (ISLRN: 682-113-241-701-0) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0309/ ELRA-S0310 TC-STAR Spanish Baseline Male Speech Database (ISLRN: 736-021-086-598-0) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0310/ ELRA-S0311 TC-STAR Bilingual Voice-Conversion Spanish Speech Database (ISLRN: 254-311-004-570-0) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0311/ ELRA-S0312 TC-STAR Bilingual Voice-Conversion English Speech Database (ISLRN: 522-613-023-181-1) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0312/ ELRA-S0313 TC-STAR Bilingual Expressive Speech Database (ISLRN: 088-656-828-489-3) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0313/ ELRA-S0336 Spanish Festival voice male (ISLRN: 868-352-143-949-9) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0336/ ELRA-S0337 Spanish Festival voice female (ISLRN: 396-262-481-019-0) For more information, see: http://catalogue.elra.info/en-us/repository/browse/ELRA-S0337/ For more information on the catalogue, please contact Valérie Mapelli mailto:mapelli@elda.org If you would like to enquire about having your resources distributed by ELRA, please do not hesitate to contact us. Visit our On-line Catalogue: http://catalog.elra.info
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| 5-2-26 | USC 75-Speaker Speech MRI Database. Multispeaker speech production articulatory datasets of vocal tract MRI video A USC Multispeaker speech production articulatory datasets of vocal tract MRI video Other speech production datasets of articulatory data that are also freely available include a TIMIT articulatory data corpus and emotional speech production data, all available from: https://sail.usc.edu/span/resources.html
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| 5-2-27 | Annoted tweet corpus , ELDA and INSA Rouen ELDA and INSA Rouen Normandie partner to release the Annotated tweet corpus in Arabizi, French and English ELDA and INSA Rouen Normandie are pleased to announce the release of the Annotated tweet corpus in Arabizi, French and English. This corpus was built by ELDA on behalf of INSA Rouen Normandie (Normandie Université, LITIS team), in the framework of the SAPhIRS project (System for the Analysis of Information Propagation in Social Networks), funded by the DGE (Direction Générale des Entreprises, France) through the RAPID programme (2017-2020). This project aimed at studying the mechanisms of information and opinion propagation within social networks: identifying influential leaders, detecting channels for disseminating information and opinion. The purpose of the corpus constitution, completed in 2020, was to collect and annotate tweets in 3 languages (Arabizi, French and English) for 3 predefined themes (Hooliganism, Racism, Terrorism). The annotated tweet corpus in Arabizi, French and English can be found in the ELRA Catalogue under the following links and references:
For more information and/or questions, please write to contact@elda.org.
About INSA Rouen Normandie As a leading regional institute for research and higher education in engineering and among the main French establishments, INSA Rouen Normandie holds a major place in the landscape of engineering education in France. Its mission includes education (11 engineering courses including 4 apprenticeship programs, 2 master’s specialization and 7 masters), research (8 laboratories) and the spreading of scientific culture in the following fields of expertise: information systems, big data, mathematics, chemistry and processes, risks management and industrial site recovery, energy, propulsion systems, mechanics, industrial performance, civil engineering and urban design and planning. INSA Rouen Normandie graduates almost 400 engineers and is a member of the INSA group. It is closely tied to the world of industry and has established a large number of partnerships with international organisations. To find out more about INSA Rouen Normandie, please visit: https://www.insa-rouen.fr/
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| 5-2-28 | LR Agreement between ELRA and Datatang Paris, France, October 24, 2022/
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| 5-2-29 | Biomedical language model DrBERT We are proud to announce our first biomedical language model for French called DrBERT. It's now available on HuggingFace and Arxiv (https://arxiv.org/abs/2304.00958).
You can now use the model on your own documents and get state-of-the-art performances in only 3 lines of code.
Check out the:
- Project website: https://drbert.univ-avignon.fr/
- Hugging Face models: https://huggingface.co/Dr-BERT
Our model was trained on 128 GPU from Jean-Zay and assessed on 11 distinct practical biomedical tasks for French language, which came from public and private data. These tasks include : Named Entity Recognition (NER), Part-Of-Speech tagging (POS), binary/multi-class/multi-label classification, and multiple-choice question answering. The outcomes revealed that DrBERT enhanced the performance of most tasks compared to prior techniques, indicating that from-scratch pre-trained strategy is still the most effective for BERT language models on French Biomedical.
Tutorials about biomedical natural language processing are coming soon, stay tuned !!
With Yanis Labrak (LIA / Zenidoc), Adrien Bazoge (LS2N), Richard Dufour (LS2N), Mickael Rouvier (LIA), Emmanuel Morin (LS2N), Béatrice Daille (LS2N) and Pierre-Antoine Gourraud (Nantes University / CHU Nantes).
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| 5-2-30 | A respiratory sounds and symptoms dataset: Coswara We are happy to share information on the release of open-access dataset on respiratory sound samples with the community. Hoping the dataset finds use in understanding respiratory sounds (breathing, cough, vowel phonation, and speech) and building solutions for healthcare.
It will be great if this information can be shared with the ISCA community!
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(Behind the paper) Blog post: https://researchdata.springernature.com/posts/the-covid-19-connection-breathing-cough-and-speech-audio-dataset-for-respiratory-healthcare
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| 5-2-31 | ELDA releases air-traffic control data from ATCO2 Project Press Release - Immediate
ELDA releases air-traffic control data from ATCO2 Project ELDA is pleased to announce the release of ATCO2 Project Data. ATCO2 project collected the real-time voice communication between air-traffic controllers and pilots available either directly through publicly accessible radio frequency channels or indirectly from air-navigation service providers (ANSPs). In addition to the voice communication data, contextual information is available in a form of metadata (i.e. surveillance data). The dataset consists of two subsets:
The dataset can be found in the ELRA Catalogue of Language Resources under the following links and references: ATCO 2 Project Data, ISLRN: 589-403-577-685-7 For more information and/or questions, please write to contact@elda.org.
About ATCO2 project ATCO2 project (Automated data collection and semi-supervised processing framework for deep learning) aims at developing a unique platform allowing to collect, organize and pre-process air-traffic control (voice communication) data from air space. It has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864702. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The following partners collaborated on this project: IDIAP Research Institute (Switzerland), Opensky Network (Switzerland), ELDA (France), ReplayWell (Czech Republic), Romagnatech (Italy), Universität des Saarlandes (Germany), Brno University of Technology (Czech Republic). To find out more about ATCO2 project, please visit: https://www.atco2.org/
About ELDA The Evaluation and Language resources Distribution Agency (ELDA) identifies, collects, markets, and distributes language resources, along with the dissemination of general information in the field of Human Language Technologies (HLT). ELDA has considerable knowledge and skills in HLT applications. ELDA is part of major French, European and international projects in the field of HLT. To find out more about ELDA, please visit our web site: http://www.elda.org/
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| 5-2-32 | AVID (Aalto Vocal Intensity Database): An open speech/electroglottography repository for machine learning -based studies on vocal intensity AVID (Aalto Vocal Intensity Database): An open speech/electroglottography repository for machine learning -based studies on vocal intensity
AVID is an open database, which includes speech and electroglottography (EGG) signals produced by 50 speakers (25 males, 25 females). The speakers varied their vocal intensity in four categories (soft, normal, loud and very loud). Each speaker produced 25 isolated sentences in English and read two paragraphs of text using the four intensity modes. These speaking tasks were repeated twice in two sessions. Recordings were conducted using a constant mouth-to-microphone distance and by recording a sound pressure level (SPL) calibration tone. The speech data is labeled sentence-wise with a total of 19 labels (1 categorical intensity category label and 18 continuous SPL labels). By launching the open AVID repository, the authors would like to raise awareness of the speech and voice research communities for machine learning (ML) - based studies of vocal intensity. We are particularly advocating the utilization of ML in a scenario where the original intensity information of speech is lost because the signal has been recorded without SPL calibration and is therefore presented on an arbitrary amplitude scale. In order to demonstrate how ML can be used together with the AVID database for these kinds of research problems, the interested reader is referred to our article (Alku, Kodali, Laaksonen, Kadiri, “AVID: A speech database for machine learning studies on vocal intensity”, Speech Communication, Vol. 157, Article 103039, 2024). The AVID database is freely available at: https://zenodo.org/records/10524873
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