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Updated: Nov 5, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Considering Performance in the Automated and Manual Coding of Sociolinguistic Variables: Lessons From Variable (ING).

Tyler Kendall1, Charlotte Vaughn1,2, Charlie Farrington1,3

  • 1Linguistics Department, University of Oregon, Eugene, OR, United States.

Frontiers in Artificial Intelligence
|May 17, 2021
PubMed
Summary

Automated methods show promise for coding sociolinguistic variables like English (ING) pronunciation. However, expect variability even with human-coded data, highlighting the need for careful validation in language variation studies.

Keywords:
English variable (ING)automated codingclassificationforced alignmentimpressionistic codingmachine learningsociolinguistic variables

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Area of Science:

  • Sociolinguistics
  • Computational Linguistics
  • Speech Processing

Background:

  • Impressionistic coding of sociolinguistic variables, such as English (ING) pronunciation, is crucial for language variation and change studies.
  • Automating sociolinguistic data analysis has advanced, but coding features like (ING) without clear acoustic correlates lags behind other phonetic features.

Purpose of the Study:

  • To explore computational methods for automatically coding variable (ING) pronunciation in speech recordings.
  • To compare automated coding performance with human coders and assess the impact on linguistic and social factor analyses.

Main Methods:

  • Utilized automatic speech recognition (ASR) procedures, specifically forced alignment with the Montreal Forced Aligner.
  • Employed supervised machine learning algorithms, including linear and radial support vector machines, and random forests.
  • Assessed algorithm performance through agreement with human coders and impact on sociolinguistic analysis outcomes.

Main Results:

  • Automated coding methods demonstrate potential for analyzing sociolinguistic variables like (ING).
  • Variability in results is expected, even with meticulously human-coded data, underscoring the importance of robust validation.
  • Baseline performance data for both automated and manual coding methods were established.

Conclusions:

  • Automated coding of sociolinguistic variables like (ING) is feasible and promising.
  • Further research is needed to establish "gold standards" for training and testing automated procedures.
  • The study provides valuable baseline data and highlights considerations for integrating automated methods into sociolinguistic research.