A computational model of early language acquisition from audiovisual experiences of young infants

Okko Räsänen, Khazar Khorrami

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Earlier research has suggested that human infants might use statistical dependencies between speech and non-linguistic multimodal input to bootstrap their language learning before they know how to segment words from running speech. However, feasibility of this hypothesis in terms of real-world infant experiences has remained unclear. This paper presents a step towards a more realistic test of the multimodal bootstrapping hypothesis by describing a neural network model that can learn word segments and their meanings from referentially ambiguous acoustic input. The model is tested on recordings of real infant-caregiver interactions using utterance-level labels for concrete visual objects that were attended by the infant when caregiver spoke an utterance containing the name of the object, and using random visual labels for utterances during absence of attention. The results show that beginnings of lexical knowledge may indeed emerge from individually ambiguous learning scenarios. In addition, the hidden layers of the network show gradually increasing selectivity to phonetic categories as a function of layer depth, resembling models trained for phone recognition in a supervised manner.

Original languageEnglish
Title of host publicationProceedings of Interspeech
PublisherInternational Speech Communication Association
Pages3594-3598
Number of pages5
Volume2019-September
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInterspeech - Graz, Austria
Duration: 15 Sep 201919 Sep 2019
https://www.interspeech2019.org/

Publication series

NameInterspeech - Annual Conference of the International Speech Communication Association
ISSN (Electronic)2308-457X

Conference

ConferenceInterspeech
CountryAustria
CityGraz
Period15/09/201919/09/2019
Internet address

Keywords

  • Computational modeling
  • L1 acquisition
  • Language acquisition
  • Lexical learning
  • Phonetic learning

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  • Projects

    Computational basis of contextually grounded language acquisition in humans and machines

    Räsänen, O.

    31/12/201731/12/2017

    Project: Academy of Finland: Other research funding

    Computational basis of contextually grounded language acquisition in humans and machines

    Räsänen, O.

    31/12/201731/12/2017

    Project: Academy of Finland: Other research funding

    Cite this

    Räsänen, O., & Khorrami, K. (2019). A computational model of early language acquisition from audiovisual experiences of young infants. In Proceedings of Interspeech (Vol. 2019-September, pp. 3594-3598). (Interspeech - Annual Conference of the International Speech Communication Association). International Speech Communication Association. https://doi.org/10.21437/Interspeech.2019-1523