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1. How much does prosody help word segmentation? A simulation study on infant-directed speech

Author

Emmanuel Dupoux, Reiko Mazuka, Alejandrina Cristia, Bogdan Ludusan, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Department of Psychology and Neuroscience, Duke University [Durham], Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Apprentissage machine et développement cognitif (CoML), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), École des hautes études en sciences sociales (EHESS), The research reported in this paper was partly funded by JSPS Grant-in-Aid for Scientific Research (16H06319, 20H05617) and MEXT Grant-in-aid on Innovative Areas #4903 (Co-creative Language Evolution), 17H06382 to RM. It was also supported by the European Research Council (ERC-2011-AdG-295810 BOOTPHON), the Agence Nationale pour la Recherche (ANR-17-CE28-0007 LangAge, ANR-16-DATA-0004 ACLEW, ANR-14-CE30-0003 MechELex, ANR-17-EURE-0017 Frontcog, ANR-10-IDEX-0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute). ED is further grateful to the CIFAR (Learning in Machines and Brain), BL to the Canon Foundation in Europe, and AC to the JS McDonnell Foundation., ANR-17-CE28-0007,LangAge,Différences dans l'apprenabilité du langage selon l'âge(2017), ANR-16-DATA-0004,ACLEW,Analyzing Child Language Experiences Around the World(2016), ANR-14-CE30-0003,MechELex,Méchanismes d'acquisition lexicale précoce(2014), ANR-17-EURE-0017,FrontCog,Frontières en cognition(2017), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 295810,EC:FP7:ERC,ERC-2011-ADG_20110406,BOOTPHON(2012), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris)

Subjects
Linguistics and Language, Cognitive Neuroscience, Speech recognition, Experimental and Cognitive Psychology, Prosody, Infant-directed speech, 050105 experimental psychology, Language and Linguistics, Speech Acoustics, Task (project management), Infant language acquisition, Developmental and Educational Psychology, Humans, Learning, Speech, 0501 psychology and cognitive sciences, Segmentation, Computer Simulation, ComputingMilieux_MISCELLANEOUS, Computational model, 05 social sciences, Text segmentation, Infant, acquisition, [SCCO.LING]Cognitive science/Linguistics, Word segmentation, [SCCO.PSYC]Cognitive science/Psychology, Speech Perception, Infant language, Cues, Heuristics, Psychology, Word (computer architecture), 050104 developmental & child psychology
Abstract

International audience; Infants come to learn several hundreds of word forms by two years of age, and it is possible this involves carving these forms out from continuous speech. It has been proposed that the task is facilitated by the presence of prosodic boundaries. We revisit this claim by running computational models of word segmentation, with and without prosodic information, on a corpus of infant-directed speech. We use five cognitively-based algorithms, which vary in whether they employ a sub-lexical or a lexical segmentation strategy and whether they are simple heuristics or embody an ideal learner. Results show that providing expert-annotated prosodic breaks does not uniformly help all segmentation models. The sub-lexical algorithms, which perform more poorly, benefit most, while the lexical ones show a very small gain. Moreover, when prosodic information is derived automatically from the acoustic cues infants are known to be sensitive to, errors in the detection of the boundaries lead to smaller positive effects, and even negative ones for some algorithms. This shows that even though infants could potentially use prosodic breaks, it does not necessarily follow that they should incorporate prosody into their segmentation strategies, when confronted with realistic signals.

Published
2022

2. Does Infant-Directed Speech Help Phonetic Learning? A Machine Learning Investigation

Author

Reiko Mazuka, Emmanuel Dupoux, Bogdan Ludusan, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Universität Bielefeld = Bielefeld University, Department of Psychology and Neuroscience, Duke University [Durham], Apprentissage machine et développement cognitif (CoML), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de sciences cognitives et psycholinguistique (LSCP), CIFAR program in Learning in Machines & Brains CIFAR LMB program, The research reported in this paper was partly funded by JSPS Grant-in-Aid for Scientific Research (16H06319, 20H05617) and MEXT Grant-in-Aid on Innovative Areas #4903 (Co-creative Language Evolution), 17H06382 to R. Mazuka. The work of E. Dupoux in his EHESS role was supported by the European Research Council (ERC-2011-AdG-295810 BOOTPHON) the Agence Nationale pour la Recherche (ANR-10-LABX-0087 IEC, ANR-10-IDEX0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute), and CIFAR (Learning in Machines and Brain). Part of the work was conducted while E. Dupoux was a visiting scientist at DeepMind and Facebook. B. Ludusan was also supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 799022., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-17-EURE-0017,FrontCog,Frontières en cognition(2017), ANR-10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), European Project: 295810,EC:FP7:ERC,ERC-2011-ADG_20110406,BOOTPHON(2012), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Functional role, Adult, Computer science, Cognitive Neuroscience, Experimental and Cognitive Psychology, Machine learning, computer.software_genre, Infant-directed speech, 050105 experimental psychology, Read speech, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Artificial Intelligence, Robustness (computer science), Phonetics, Concept learning, Vowel, Humans, Speech, 0501 psychology and cognitive sciences, Speech variability, Hyperarticulation, business.industry, 05 social sciences, Phonetic learning, Infant, Reading, Speech Perception, Artificial intelligence, business, computer, Adult-directed speech, 030217 neurology & neurosurgery
Abstract

A prominent hypothesis holds that by speaking to infants in infant-directed speech (IDS) as opposed to adult-directed speech (ADS), parents help them learn phonetic categories. Specifically, two characteristics of IDS have been claimed to facilitate learning: hyperarticulation, which makes the categories more separable, and variability, which makes the generalization more robust. Here, we test the separability and robustness of vowel category learning on acoustic representations of speech uttered by Japanese adults in ADS, IDS (addressed to 18- to 24-month olds), or read speech (RS). Separability is determined by means of a distance measure computed between the five short vowel categories of Japanese, while robustness is assessed by testing the ability of six different machine learning algorithms trained to classify vowels to generalize on stimuli spoken by a novel speaker in ADS. Using two different speech representations, we find that hyperarticulated speech, in the case of RS, can yield better separability, and that increased between-speaker variability in ADS can yield, for some algorithms, more robust categories. However, these conclusions do not apply to IDS, which turned out to yield neither more separable nor more robust categories compared to ADS inputs. We discuss the usefulness of machine learning algorithms run on real data to test hypotheses about the functional role of IDS. © 2021 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).

Published
2020

3. Climbing Brain Levels of Organisation from Genes to Consciousness

Author

Jean-Pierre Changeux, CNRS UMR3571, Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Collège de France (CdF (institution)), This paper received funding from the European Union's Horizon 2020 research and innovation programmeunder grant agreement No 720270 (HBP SGA1) and SCR 130008 0, 1UE 64102 (HBP ramp up), European Project: 720270,H2020 Pilier Excellent Science,H2020-Adhoc-2014-20,HBP SGA1(2016), and Institut Pasteur [Paris] (IP)

Subjects
0301 basic medicine, Consciousness, Cognitive Neuroscience, media_common.quotation_subject, Brain, Gene Expression Regulation, Developmental, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Experimental and Cognitive Psychology, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Neuropsychology and Physiological Psychology, Climbing, Gene Regulatory Networks, Psychology, Neuroscience, Gene, 030217 neurology & neurosurgery, Transcription Factors, media_common, Epigenesis
Abstract

International audience; Given the tremendous complexity of brain organisation, here I propose a strategy that dynamically links stages of brain organisation from genes to consciousness, at four privileged structural levels: genes; transcription factors (TFs)-gene networks; synaptic epigenesis; and long-range connectivity. These structures are viewed as nested and reciprocally inter-regulated, with a hierarchical organisation that proceeds on different timescales during the course of evolution and development. Interlevel bridging mechanisms include intrinsic variation-selection mechanisms, which offer a community of bottom-up and top-down models linking genes to consciousness in a stepwise manner.

Published
2017