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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 language | English |
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Title of host publication | Proceedings of Interspeech |
Publisher | International Speech Communication Association |
Pages | 3594-3598 |
Number of pages | 5 |
Volume | 2019-September |
DOIs | |
Publication status | Published - 1 Jan 2019 |
MoE publication type | A4 Article in a conference publication |
Event | Interspeech - Graz, Austria Duration: 15 Sep 2019 → 19 Sep 2019 https://www.interspeech2019.org/ |
Publication series
Name | Interspeech - Annual Conference of the International Speech Communication Association |
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ISSN (Electronic) | 2308-457X |
Conference
Conference | Interspeech |
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Country/Territory | Austria |
City | Graz |
Period | 15/09/2019 → 19/09/2019 |
Internet address |
Keywords
- Computational modeling
- L1 acquisition
- Language acquisition
- Lexical learning
- Phonetic learning
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Computational basis of contextually grounded language acquisition in humans and machines
Räsänen, O.
31/12/2017 → 31/08/2023
Project: Academy of Finland: Other research funding
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Computational basis of contextually grounded language acquisition in humans and machines
Räsänen, O.
31/12/2017 → 31/08/2021
Project: Academy of Finland: Other research funding