Does Infant‐Directed Speech Help Phonetic Learning? A Machine Learning Investigation

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 categ...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Cognitive science Jg. 45; H. 5; S. e12946 - n/a
Hauptverfasser: Ludusan, Bogdan, Mazuka, Reiko, Dupoux, Emmanuel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley 01.05.2021
Wiley Subscription Services, Inc
Schlagworte:
Acoustic phonetics
Acoustics
Adults
Adult‐directed speech
Algorithms
Articulation
Articulation (Speech)
Artificial Intelligence
Child Language
Classification
Computer Science
Foreign Countries
Generalization
Hyperarticulation
Hypothesis Testing
Infants
Infant‐directed speech
Japanese
Japanese language
Learning algorithms
Machine Learning
Mathematics
Phonemes
Phonetic learning
Phonetics
Read speech
Robustness
Speech
Speech Communication
Speech variability
Speeches
Statistics
Tests
Usefulness
Variability
Vowels
Young Children
Japan
Phonetic learning
Read speech
Infant-directed speech
Speech variability
Adult-directed speech
Hyperarticulation
ISSN:0364-0213, 1551-6709, 1551-6709
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0364-0213
1551-6709
1551-6709
DOI:10.1111/cogs.12946