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WIDE SIGNED

Wide Incremental learning with Discrimination nEtworks

Total Cost €

0

EC-Contrib. €

0

Partnership

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 WIDE project word cloud

Explore the words cloud of the WIDE project. It provides you a very rough idea of what is the project "WIDE" about.

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Project "WIDE" data sheet

The following table provides information about the project.

Coordinator
EBERHARD KARLS UNIVERSITAET TUEBINGEN 

Organization address
address: GESCHWISTER-SCHOLL-PLATZ
city: TUEBINGEN
postcode: 72074
website: www.uni-tuebingen.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website http://quantling.org/ERC-WIDE/
 Total cost 2˙496˙875 €
 EC max contribution 2˙496˙875 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-ADG
 Funding Scheme ERC-ADG
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2022-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EBERHARD KARLS UNIVERSITAET TUEBINGEN DE (TUEBINGEN) coordinator 2˙496˙875.00

Map

 Project objective

Although homo sapiens has been endowed with language for over 50,000 years, the invention of alphabet-like scripts 3,000 years ago dominates Western linguistic thinking. Training in literacy starts in early childhood, and because of this, words and letter-like sound units can naturally seem to be the building blocks of language. The Chinese writing system highlights the cultural-specificity of this approach: characters are juxtaposed without intervening spaces, and their interpretation is highly context-dependent. Words are not singled out. And although more frequent characters contain parts indicating pronunciation, it is syllables that are referred to, not letter-like sound units.

The research proposed here seeks to break the hold that the alphabet-centric approach has on our understanding of language by exploring the idea that instead of being phone and word-based, languages use low-level properties of the acoustic signal to directly reduce uncertainty about the messages encoded in the speech signal. My work with wide learning networks (two-layer networks with many thousands of units, using the simplest possible error-driven learning rule) provides remarkable support for this suggestion: For reading and speech comprehension, their performance closely matches both the strengths and the weaknesses of human processing. Especially at a time when machine learning and artificial intelligence are moving beyond human capacity, it is a methodological imperative to study and work with algorithms reflecting both the advantages and disadvantages of human learning.

I am requesting funding to take this radically novel research program to the next level by further developing our account of auditory comprehension, by modeling more typologically diverse languages, by extending this approach to speech production, and by developing a discrimination-based language theory.

 Publications

year authors and title journal last update
List of publications.
2020 Maja Linke, Michael Ramscar
How the Probabilistic Structure of Grammatical Context Shapes Speech
published pages: 90, ISSN: 1099-4300, DOI: 10.3390/e22010090
Entropy 22/1 2020-02-12
2018 Harald Baayen, Yu-Ying Chuang, James P. Blevins
Inflectional morphology with linear mappings
published pages: 230-268, ISSN: 1871-1340, DOI: 10.1075/ml.18010.baa
The Mental Lexicon 13/2 2019-10-29
2018 Elnaz Shafaei-Bajestan, R. Harald Baayen
Wide Learning for Auditory Comprehension
published pages: 966-970, ISSN: , DOI: 10.21437/interspeech.2018-2420
Interspeech 2018 2019-10-29
2019 Baayen, R. H.
Are You Listening? Teaching a Machine to Understand Speech
published pages: , ISSN: , DOI:
Scientia 13.02.2019 2019-10-08
2018 Natalie Boll-Avetisyan, Jessie S. Nixon, Tomas O. Lentz, Liquan Liu, Sandrien van Ommen, Çağri Çöltekin, Jacolien van Rij
Neural Response Development During Distributional Learning
published pages: 1432-1436, ISSN: , DOI: 10.21437/interspeech.2018-2072
Interspeech 2018 02.09.2018 2019-10-08
2018 Jessie S. Nixon
Effective Acoustic Cue Learning Is Not Just Statistical, It Is Discriminative
published pages: 1447-1451, ISSN: , DOI: 10.21437/interspeech.2018-1024
Interspeech 2018 02.09.2018 2019-10-08
2018 Konstantin Sering, Petar Milin, R. Harald Baayen
Language comprehension as a multi-label classification problem
published pages: 339-353, ISSN: 0039-0402, DOI: 10.1111/stan.12134
Statistica Neerlandica 72/3 2019-10-08
2019 R. Harald Baayen, Yu-Ying Chuang, Elnaz Shafaei-Bajestan, James P. Blevins
The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discriminative Learning
published pages: 1-39, ISSN: 1076-2787, DOI: 10.1155/2019/4895891
Complexity 2019 2019-10-08
2019 Chuang, Yu-Ying; Vollmer, Marie-lenka; Shafaei-Bajestan, Elnaz; Gahl, Susanne; Hendrix, Peter; Baayen, Rolf Harald
On the processing of nonwords in word naming and auditory lexical decision
published pages: , ISSN: , DOI: 10.5281/zenodo.3369096
Proceedings ICPhS 2019, Melbourne, Australia 05.08.2019 2019-08-29
2019 Chuang, Yu-Ying; Sun, Ching-Chu; Fon, Janice; Baayen, Rolf Harald
Geographical variation of the merging between dental and retroflex sibilants in Taiwan Mandarin
published pages: , ISSN: , DOI: 10.5281/zenodo.3369120
Proceedings ICPhS 2019, Melbourne, Australia 05.08.2019 2019-08-29
2019 Giovanni Cassani, Yu-Ying Chuang, R. Harald Baayen
On the semantics of nonwords and their lexical category.
published pages: , ISSN: 0278-7393, DOI: 10.1037/xlm0000747
Journal of Experimental Psychology: Learning, Memory, and Cognition 18.07.2019 2019-08-29
2019 Baayen, R. H.
Understanding and producing words with high-school maths
published pages: 424-425, ISSN: , DOI:
Open Access Government 23 2019-09-02

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