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ISCApad #288 |
Friday, June 10, 2022 by Chris Wellekens |
5-1-1 | Benjamin Weiss, 'Talker Quality in Human and Machine Interaction - Modeling the Listener’s Perspective in Passive and Interactive Scenarios'. T-Labs Series in Telecommunication Services. Springer Nature, Cham. (2020) Benjamin Weiss (2020): 'Talker Quality in Human and Machine Interaction - Modeling the Listener’s Perspective in Passive and Interactive Scenarios'. T-Labs Series in Telecommunication Services. Springer Nature, Cham.
https://rd.springer.com/book/10.1007/978-3-030-22769-2
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5-1-2 | W.F.Katz, P.F.Assman, 'The Routledge Handbook of Phonetics', Routledge.
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5-1-3 | Proceedings of SLTU-CCURL2020 Dear all, we are very happy to announce that the SLTU-CCURL2020 Proceedings are available online: https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/SLTUCCURLbook.pdf
This year, LREC2020 would have featured an extraordinary event: the first joint SLTU-CCURL2020 Workshop, which was planned as a two-day workshop, with 54 papers accepted either as oral and poster presentations.
The workshop program was enriched by two tutorials and two keynote speeches.
We will miss the presentations, the discussions and the overall stimulating environment very deeply.
We are thankful to ELRA and ISCA for their support to the workshop, to our Google sponsor and to the 60 experts of the Program Committee, who worked tirelessly in order to help us to select the best papers representing a wide perspective over NLP, speech and computational linguistics addressing less-resource languages.
Looking forward to better times when we will be able to meet in person again, we hope that you will find these workshop proceedings relevant and stimulating for your own research.
With our best wishes,
Claudia Soria, Laurent Besacier, Dorothee Beermann, and Sakriani Sakti
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5-1-4 | J.Blauert, J. Braasch, 'The Technology of Binaural Understanding', Springer and ASA-Press My name is Jens Blauert, and you may recall me as an ESCA-Founder and ISCA-Goldmedalist. Although I am professor emeritus since many years, I am still active in science.
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5-1-5 | Weiss B., Trouvain J., Barkat-Defradas M., Ohala J.J., 'Voice Attractiveness', Springer 2021Voice AttractivenessVoice attractivenessStudies on Sexy, Likable, and Charismatic Speakers
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5-1-6 | Diana Sidtis, Foundations of Familiar Language, Wiley -----
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5-2-1 | Linguistic Data Consortium (LDC) update (May 2022)
In this newsletter: 30th Anniversary Highlight: Penn Treebank New publications: * (2) Samrómur Icelandic Speech 1.0 was developed by the Language and Voice Lab, Reykjavik University in cooperation with Almannarómur, Center for Language Technology. The corpus contains 145 hours of Icelandic prompted speech from 8,392 speakers representing 100,000 utterances.
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 (May 2022) We are happy to announce that 1 new written corpus, 4 new bilingual lexicons and 1 new monolingual lexicon are now available in our catalogue. Annotated tweet corpus in Arabizi, French and English ISLRN: 482-848-308-105-6 The purpose of the annotated tweet corpus in Arabizi, French and English constitution, completed in 2020, was to collect and annotate tweets in 3 languages (Arabizi, French and English) for 3 predefined themes (Hooliganism, Racism, Terrorism). It consists of 17,103 sequences annotated from 585,163 tweets (196,374 in English, 254,748 in French and 134,041 in Arabizi), including the themes “Others” and “Incomprehensible”. Among these sequences, 4,578 sequences having at least 20 tweets annotated with the 3 predefined themes (Hooliganism, Racism and Terrorism) were obtained, including 1,866 sequences with an opinion change. They are distributed as follows: 2,141 sequences in English (57,655 tweets), 1,942 sequences in French (48,854 tweets) and 495 sequences in Arabizi (21,216 tweets). A sub-corpus of 8,733 tweets (1,209 in English, 3,938 in French and 3,585 in Arabizi) annotated as “hateful”, according to topic/opinion annotations and by selecting tweets that contained insults, is also provided. A Bilingual English-Ukrainian Lexicon of Named Entities Extracted from Wikipedia ISLRN: 110-617-195-245-4 The bilingual English-Ukrainian lexicon of named entities uses Wikipedia metadata as a source. The extracted named entity pairs are classified into five classes: PERSON, ORGANIZATION, LOCATION, PRODUCT, and MISC (miscellaneous). The lexicon consists of 624,168 pairs and comes in two formats: csv and xml. ArabLEX set of data ArabLEX set of data consists of 4 databases dedicated to Arabic language: ArabLEX: Database of Arabic General Vocabulary (DAG) ISLRN: 879-334-992-724-8 A comprehensive full-form lexicon of Arabic general vocabulary including all inflected, conjugated and cliticized forms. Each entry is accompanied by a rich set of morphological, grammatical, and phonological attributes. Ideally suited for NLP applications, DAG provides precise phonemic transcriptions and full vowel diacritics designed to enhance Arabic speech technology. Quantity and size: 87,930,738 lines / 24,399 MB (23.8 GB) |