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Related Experiment Video

Updated: Oct 12, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Using Rational Models to Interpret the Results of Experiments on Accent Adaptation.

Maryann Tan^1,2, Xin Xie^2,3, T Florian Jaeger^2,4

¹Centre for Research on Bilingualism, Department of Swedish Language & Multilingualism, Stockholm University, Stockholm, Sweden.

Frontiers in Psychology

|November 22, 2021

Summary

This summary is machine-generated.

Listeners adapt to non-native accents by learning speech statistics. This study shows distributional learning explains accent adaptation, improving speech comprehension even with unfamiliar accents.

Keywords:

L2 speech adaptation computational modeling distributional learning ideal observer non-native speech rational cognition

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Last Updated: Oct 12, 2025

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Area of Science:

Psycholinguistics
Computational Linguistics
Speech Perception

Background:

Exposure to unfamiliar non-native speech often enhances comprehension.
Distributional learning, inferring phonetic cue statistics, is a proposed mechanism for this adaptation.
Existing models fit native speech but haven't been tested on non-native accents.

Purpose of the Study:

To test if distributional learning explains adaptation to non-native accents.
To determine if statistical properties of speech cues predict comprehension improvements.
To apply computational models to non-native accent adaptation.

Main Methods:

Utilized ideal observer models to analyze speech cue distributions.
Compared listener adaptation to predictions based solely on phonetic statistics.
Focused on the statistical properties of native and non-native accented speech.

Main Results:

Results suggest distributional learning adequately explains adaptation to non-native accents.
Listener adaptation can be understood by inferring statistics from speech cue distributions.
The computational model provides a predictive framework for accent adaptation.

Conclusions:

Distributional learning is a viable mechanism for non-native accent adaptation.
Understanding speech statistics is key to improving comprehension of accented speech.
The shared code and data facilitate further research in this area.