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Georgia Loukatou¹, Sabine Stoll², Damian Blasi³

¹LSCP, Département d'études cognitives, ENS, EHESS, CNRS, PSL University, 75005 Paris, France.

|December 17, 2021

Summary

This summary is machine-generated.

Infants may find it harder to segment speech in languages with complex word structures. Algorithm type and evaluation level significantly impact speech segmentation performance more than morphological complexity.

Keywords:

Artificial language Computational modeling Cross-linguistic variation Language acquisition Morphology Statistical learning Word segmentation

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Area of Science:

Developmental psycholinguistics
Computational linguistics
Language acquisition

Background:

Infants must segment continuous speech into words and morphemes.
Previous studies focused on English, where word and morpheme boundaries often align.
Many languages have complex morphology where word and morpheme boundaries diverge, posing segmentation challenges.

Purpose of the Study:

To investigate how infants segment words and morphemes in languages with varying morphological complexity.
To compare the performance of different word segmentation algorithms across diverse linguistic structures.
To determine the influence of morphological complexity on speech segmentation accuracy.

Main Methods:

Utilized corpora from Chintang (Sino-Tibetan) and Japanese, varying in inflectional complexity.
Included artificial languages with controlled morphological complexity (morphemes per word).
Evaluated two baseline and three diverse word segmentation algorithms (distributional and lexicon-based) on word and morpheme levels.

Main Results:

Speech segmentation performance was better for morphologically simpler languages.
Segmentation accuracy decreased with increased inflectional complexity.
The impact of morphological complexity was less significant than the choice of algorithm and evaluation level.

Conclusions:

While morphological complexity affects speech segmentation, its influence is moderate.
Algorithm type and evaluation metrics are crucial factors in speech segmentation research.
Future infant speech segmentation studies should prioritize investigating algorithmic signatures over cross-linguistic complexity differences.