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ISCApad #250 |
| Friday, April 12, 2019 by Chris Wellekens |
5 Resources
| 5-1-1 | Pejman Mowlaee et al., 'Phase-Aware Signal Processing in Speech Communication: Theory and Practice', Wiley 2016Phase-Aware Signal Processing in Speech Communication: Theory and Practice
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1119238811.html
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| 5-1-2 | Jean Caelen, Anne Xuereb, 'Dialogue : altérité, interaction, énaction'
Jean Caelen,Anne Xuereb Dialogue : altérité, interaction, énaction Editions universitaires européennes
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| 5-1-3 | Bäckström, Tom (with Guillaume Fuchs, Sascha Disch, Christian Uhle and Jeremie Lecomte), 'Speech Coding with Code-Excited Linear Prediction', Springer
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| 5-1-4 | Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey (Eds), 'New Era for Robust Seech Recognition', Springer. Shinji Watanabe, Marc Delcroix, Florian Metze, John R. Hershey (Eds), 'New Era for Robust Seech Recognition', Springer. https://link.springer.com/book/10.1007%2F978-3-319-64680-0
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| 5-1-5 | Fabrice Marsac, Rudolph Sock, CONSÉCUTIVITÉ ET SIMULTANÉITÉ en Linguistique, Langues et Parole, L'Harmattan,France Nous avons le plaisir de vous annoncer la parution du volume thématique « CONSÉCUTIVITÉ ET SIMULTANÉITÉ en Linguistique, Langues et Parole » dans la Collection Dixit Grammatica (L’Harmattan, France) :
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| 5-1-6 | Emmanuel Vincent (Editor), Tuomas Virtanen (Editor), Sharon Gannot (Editor), 'Audio Source Separation and Speech Enhancement', Wiley Emmanuel Vincent (Editor), Tuomas Virtanen (Editor), Sharon Gannot (Editor), Audio Source Separation and Speech Enhancement:
ISBN: 978-1-119-27989-1 October 2018 504 pages
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| 5-1-7 | Jen-Tzung Chien, 'Source Separation and Machine Learning', Academic Press Jen-Tzung Chien, 'Source Separation and Machine Learning', Academic Press
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| 5-1-8 | Ingo Feldhausen, « Methods in prosody: A Romance language perspective », Language Science Press (open access) Nous sommes heureux de vous annoncer la parution d'un recueil validé par un comité de lecture et consacré aux méthodes de recherche en prosodie. Cet ouvrage est intitulé « Methods in prosody: A Romance language perspective ». Il est publié par Language Science Press, une maison d’édition open access. Le livre peut-être téléchargé gratuitement en cliquant sur le lien suivant : http://langsci-press.org/catalog/book/183 La table des matières est la suivante : --------------------------------------------------------------------------------------------------------- Introduction Foreword I Large corpora and spontaneous speech 1) Using large corpora and computational tools to describe prosody: An 2) Intonation of pronominal subjects in Porteño Spanish: Analysis of II Approaches to prosodic analysis 3) Multimodal analyses of audio-visual information: Some methods and 4) The realizational coefficient: Devising a method for empirically 5) On the role of prosody in disambiguating wh-exclamatives and III Elicitation methods 6) The Discourse Completion Task in Romance prosody research: Status 7) Describing the intonation of speech acts in Brazilian Portuguese: Indexes 263 --------------------------------------------------------------------------------------------------------- N'hésitez pas à diffuser la parution de cet ouvrage auprès de vos collègues qui pourraient s'y intéresser. Bien cordialement, Ingo Feldhausen
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| 5-1-9 | Nigel Ward, 'Prosodic Patterns in English Conversation', Cambridge University Press, 2019 Prosodic Patterns in English Conversation Nigel G. Ward, Professor of Computer Science, University of Texas at El Paso Cambridge University Press, 2019.
Spoken language is more than words: it includes the prosodic features and patterns that speakers use, subconsciously, to frame meanings and achieve interactional goals. Thanks to the application of simple processing techniques to spoken dialog corpora, this book goes beyond intonation to describe how pitch, timing, intensity and voicing properties combine to form meaningful temporal configurations: prosodic constructions. Combining new findings with hitherto-scattered observations from diverse research traditions, this book enumerates twenty of the principal prosodic constructions of English.
http://www.cambridge.org/ward/ nigel@utep.edu http://www.cs.utep.edu/nigel/
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| 5-2-1 | Linguistic Data Consortium (LDC) update (March 2019) March 2019 Newsletter
In this newsletter:
Call for Papers – LTC 2019, LREC 2020
New Publications:
CALLFRIEND Egyptian Arabic Second Edition
Penn Discourse Treebank Version 3.0
The 9th Language & Technology Conference (LTC 2019) will take place on May 17-19, 2019, at the Adam Mickiewicz University in Pozna?, Poland. LTC addresses Human Language Technologies as a challenge for computer science, linguistics, and related fields. Conference papers are due next week on Wednesday, March 20, 2019 (midnight, any time zone). For more information, visit the conference webpage.
The 12th Conference on Language Resources and Evaluation (LREC 2020) will take place on May 13-15, 2020, at the Palais du Pharo in Marseille, France. LREC aims to provide an overview of the state-of-the-art, explore new R&D directions and emerging trends, and exchange information regarding language resources and their applications, evaluation methodologies, and tools. Conference papers are due by November 25, 2019. For more information, including conference topics, visit the conference webpage.
(1) CALLFRIEND Egyptian Arabic Second Edition was developed by LDC and consists of approximately 25 hours of unscripted telephone conversations between native speakers of Egyptian Arabic. This second edition updates the audio files to wav format, simplifies the directory structure, and adds documentation and metadata. The first edition is available as CALLFRIEND Egyptian Arabic (LDC96S49).
All data were collected before July 1997. Participants could speak with a person of their choice on any topic; most called family members and friends. All calls originated in North America. The recorded conversations last up to 30 minutes.
CALLFRIEND Egyptian Arabic Second Edition is distributed via web download.
2019 Subscription Members will automatically receive copies of this corpus. 2019 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for $1,000.
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(2) Penn Discourse Treebank Version 3.0 is the third release in the Penn Discourse Treebank project, the goal of which is to annotate the Wall Street Journal (WSJ) section of Treebank-2 (LDC95T7) with discourse relations. Penn Discourse Treebank Version 2 (LDC2008T05) contains over 40,600 tokens of annotated relations. In Version 3, an additional 13,000 tokens were annotated, certain pairwise annotations were standardized, new senses were included, and the corpus was subject to a series of consistency checks.
This corpus contains two tools: (1) The Annotator, used for annotation and adjudication, and which can also be used for viewing the corpus; and (2) The Conversion Tool for converting Version 2 annotation files into the Version 3 format.
The documentation directory contains a manual describing what is new in Version 3 and how Version 3 differs from Version 2; the methods and guidelines used in annotating PDTB Version 3; and a range of statistics on the tokens, including the frequency of each connective, its sense labels, and its modifiers. More information about the corpus and research carried out by the developers and others using the corpus can be found on the PDTB website.
Penn Discourse Treebank Version 3.0 is distributed via web download.
2019 Subscription Members will automatically receive copies of this corpus. 2019 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for $1,000.
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(3) VAST Chinese Speech and Transcripts was developed by LDC for the VAST (Video Annotation for Speech Technologies) project and is comprised of approximately 29 hours of Mandarin Chinese audio extracted from amateur video content harvested from the web and corresponding time-aligned transcripts.
Audio files were transcribed using XTrans, which supports manual transcription across multiple channels, languages, and platforms. Transcribers followed a Quick-Rich Transcription style; transcription guidelines are included in this release.
The aim of the VAST project was to collect and annotate data in several languages to support the development of speech technologies such as speech activity detection, language identification, speaker identification, and speech recognition.
VAST Chinese Speech and Transcripts is distributed via web download.
2019 Subscription Members will automatically receive copies of this corpus. 2019 Standard Members may request a copy as part of their 16 free membership corpora. Non-members may license this data for $1,000.
Membership Office
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 (October 2018) ELRA - Language Resources Catalogue - Update
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We are happy to announce that 2 new Written Corpora and 4 new Speech resources are now available in our catalogue. ELRA-W0126 Training and test data for Arabizi detection and transliteration ISLRN: 986-364-744-303-9 The dataset is composed of : a collection of mixed English and Arabizi text intended to train and test a system for the automatic detection of code-switching in mixed English and Arabizi texts ; and a set of 3,452 Arabizi tokens manually transliterated into Arabic, intended to train and test a system that performs Arabizi to Arabic transliteration. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-W0126/ ELRA-W0127 Normalized Arabic Fragments for Inestimable Stemming (NAFIS)
ISLRN: 305-450-745-774-1 This is an Arabic stemming gold standard corpus composed by a collection of 37 sentences, selected to be representative of Arabic stemming tasks and manually annotated. Compiled sentences belong to various sources (poems, holy Quran, books, and periodics) of diversified kinds (proverb and dictum, article commentary, religious text, literature, historical fiction). NAFIS is represented according to the TEI standard. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-W0127/ ELRA-S0396 Mbochi speech corpus ISLRN: 747-055-093-447-8 This corpus consists of 5131 sentences recorded in Mbochi, together with their transcription and French translation, as well as the results from the work made during JSALT workshop: alignments at the phonetic level and various results of unsupervised word segmentation from audio. The audio corpus is made up of 4,5 hours, downsampled at 16kHz, 16bits, with Linear PCM encoding. Data is distributed into 2 parts, one for training consisting of 4617 sentences, and one for development consisting of 514 sentences. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-S0396/ ELRA-S0397 Chinese Mandarin (South) database ISLRN: 503-886-852-083-2 This database contains the recordings of 1000 Chinese Mandarin speakers from Southern China (500 males and 500 females), from 18 to 60 years? old, recorded in quiet studios. Recordings were made through microphone headsets and consist of 341 hours of audio data (about 30 minutes per speaker), stored in .WAV files as sequences of 48 KHz Mono, 16 bits, Linear PCM. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-S0397/ ELRA-S0398 Chinese Mandarin (North) database
ISLRN: 353-548-770-894-7 This database contains the recordings of 500 Chinese Mandarin speakers from Northern China (250 males and 250 females), from 18 to 60 years? old, recorded in quiet studios. Recordings were made through microphone headsets and consist of 172 hours of audio data (about 30 minutes per speaker), stored in .WAV files as sequences of 48 KHz Mono, 16 bits, Linear PCM. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-S0398/ ELRA-S0401 Persian Audio Dictionary ISLRN: 133-181-128-420-9 This dictionary consists of more than 50,000 entries (along with almost all wordforms and proper names) with corresponding audio files in MP3 and English transliterations. The words have been recorded with standard Persian (Farsi) pronunciation (all by a single speaker). This dictionary is provided with its software. For more information, see: http://catalog.elra.info/en-us/repository/browse/ELRA-S0401/ 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 the Universal Catalogue: http://universal.elra.info Archives of ELRA Language Resources Catalogue Updates: http://www.elra.info/en/catalogues/language-resources-announcements/
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| 5-2-3 | Speechocean – update (April 2019)
Cantonese Speech Recognition Corpus --- Speechocean
Speechocean: A.I. Data Resource & Service Supplier
At present, we are capable to provide around 8000 hours Cantonese speech recognition corpus, including Mainland Cantonese and Hong Kong Cantonese. Please check the form below: http://kingline.speechocean.com
More Information
If you have any further inquiries, please do not hesitate to contact us. Web: http://en.speechocean.com/ Email: marketing@speechocean.com
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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 | Fearless Steps Corpus (University of Texas, Dallas) Fearless Steps Corpus John H.L. Hansen, Abhijeet Sangwan, Lakshmish Kaushik, Chengzhu Yu Center for Robust Speech Systems (CRSS), Eric Jonsson School of Engineering, The University of Texas at Dallas (UTD), Richardson, Texas, U.S.A.
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| 5-2-11 | 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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| 5-2-12 | 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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| 5-3-1 | Release of the version 2 of FASST (Flexible Audio Source Separation Toolbox).Release of the version 2 of FASST (Flexible Audio Source Separation Toolbox). http://bass-db.gforge.inria.fr/fasst/ This toolbox is intended to speed up the conception and to automate the implementation of new model-based audio source separation algorithms. It has the following additions compared to version 1: * Core in C++ * User scripts in MATLAB or python * Speedup * Multichannel audio input We provide 2 examples: 1. two-channel instantaneous NMF 2. real-world speech enhancement (2nd CHiME Challenge, Track 1)
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| 5-3-2 | Cantor Digitalis, an open-source real-time singing synthesizer controlled by hand gestures. We are glad to announce the public realease of the Cantor Digitalis, an open-source real-time singing synthesizer controlled by hand gestures. It can be used e.g. for making music or for singing voice pedagogy. A wide variety of voices are available, from the classic vocal quartet (soprano, alto, tenor, bass), to the extreme colors of childish, breathy, roaring, etc. voices. All the features of vocal sounds are entirely under control, as the synthesis method is based on a mathematic model of voice production, without prerecording segments. The instrument is controlled using chironomy, i.e. hand gestures, with the help of interfaces like stylus or fingers on a graphic tablet, or computer mouse. Vocal dimensions such as the melody, vocal effort, vowel, voice tension, vocal tract size, breathiness etc. can easily and continuously be controlled during performance, and special voices can be prepared in advance or using presets. Check out the capabilities of Cantor Digitalis, through performances extracts from the ensemble Chorus Digitalis: http://youtu.be/_LTjM3Lihis?t=13s. In pratice, this release provides:
Regards,
The Cantor Digitalis team (who loves feedback — cantordigitalis@limsi.fr) Christophe d'Alessandro, Lionel Feugère, Olivier Perrotin http://cantordigitalis.limsi.fr/
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| 5-3-3 | MultiVec: a Multilingual and MultiLevel Representation Learning Toolkit for NLP
We are happy to announce the release of our new toolkit “MultiVec” for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes Mikolov et al. [2013b]’s word2vec features, Le and Mikolov [2014]’s paragraph vector (batch and online) and Luong et al. [2015]’s model for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification. The toolkit also includes C++ and Python libraries, that you can use to query bilingual and monolingual models.
The project is fully open to future contributions. The code is provided on the project webpage (https://github.com/eske/multivec) with installation instructions and command-line usage examples.
When you use this toolkit, please cite:
@InProceedings{MultiVecLREC2016, Title = {{MultiVec: a Multilingual and MultiLevel Representation Learning Toolkit for NLP}}, Author = {Alexandre Bérard and Christophe Servan and Olivier Pietquin and Laurent Besacier}, Booktitle = {The 10th edition of the Language Resources and Evaluation Conference (LREC 2016)}, Year = {2016}, Month = {May} }
The paper is available here: https://github.com/eske/multivec/raw/master/docs/Berard_and_al-MultiVec_a_Multilingual_and_Multilevel_Representation_Learning_Toolkit_for_NLP-LREC2016.pdf
Best regards,
Alexandre Bérard, Christophe Servan, Olivier Pietquin and Laurent Besacier
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| 5-3-4 | An android application for speech data collection LIG_AIKUMA We are pleased to announce the release of LIG_AIKUMA, an android application for speech data collection, specially dedicated to language documentation. LIG_AIKUMA is an improved version of the Android application (AIKUMA) initially developed by Steven Bird and colleagues. Features were added to the app in order to facilitate the collection of parallel speech data in line with the requirements of a French-German project (ANR/DFG BULB - Breaking the Unwritten Language Barrier).
The resulting app, called LIG-AIKUMA, runs on various mobile phones and tablets and proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). It was used for field data collections in Congo-Brazzaville resulting in a total of over 80 hours of speech.
Users who just want to use the app without access to the code can download it directly from the forge direct link: https://forge.imag.fr/frs/download.php/706/MainActivity.apk
Code is also available on demand (contact elodie.gauthier@imag.fr and laurent.besacier@imag.fr).
More details on LIG_AIKUMA can be found on the following paper: http://www.sciencedirect.com/science/article/pii/S1877050916300448
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| 5-3-5 | Web services via ALL GO from IRISA-CNRS It is our pleasure to introduce A||GO (https://allgo.inria.fr/ or http://allgo.irisa.fr/), a platform providing a collection of web-services for the automatic analysis of various data, including multimedia content across modalities. The platform builds on the back-end web service deployment infrastructure developed and maintained by Inria?s Service for Experimentation and Development (SED). Originally dedicated to multimedia content, A||GO progressively broadened to other fields such as computational biology, networks and telecommunications, computational graphics or computational physics.
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| 5-3-6 | Clickable map - Illustrations of the IPA Clickable map - Illustrations of the IPA
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| 5-3-7 | LIG-Aikuma running on mobile phones and tablets
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| 5-3-8 | Python Library Nous sommes heureux d'annoncer la mise à disposition du public de la
première bibliothèque en langage Python pour convertir des nombres écrits en
français en leur représentation en chiffres.
L'analyseur est robuste et est capable de segmenter et substituer les expressions
de nombre dans un flux de mots, comme une conversation par exemple. Il reconnaît les différentes
variantes de la langue (quantre-vingt-dix / nonante?) et traduit aussi bien les
ordinaux que les entiers, les nombres décimaux et les séquences formelles (n° de téléphone, CB?).
Nous espérons que cet outil sera utile à celles et ceux qui, comme nous, font du traitment
du langage naturel en français.
Cette bibliothèque est diffusée sous license MIT qui permet une utilisation très libre.
Sources : https://github.com/allo-media/text2num
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