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The York ACCDIST-based automatic accent recognition system (Y-ACCDIST) can screen sociophonetic corpora to objectively assess speaker similarities. This tool aids in identifying linguistic variation and corroborates traditional phonetic analysis.

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Area of Science:

  • Computational Linguistics
  • Sociophonetics
  • Phonetics

Background:

  • The York ACCDIST-based automatic accent recognition system (Y-ACCDIST) is primarily designed for forensic casework.
  • Sociophonetic research can leverage Y-ACCDIST to analyze linguistic variation within corpora.
  • The PEBL (Panjabi-English in Bradford and Leicester) corpus provides a dataset for exploring Y-ACCDIST's capabilities.

Purpose of the Study:

  • To demonstrate Y-ACCDIST's utility as a preliminary screening tool for sociophonetic corpora.
  • To explore how Y-ACCDIST outputs can objectively assess speaker similarities across linguistic varieties.
  • To investigate the corroboration between Y-ACCDIST findings and traditional phonetic analysis.

Main Methods:

  • Utilizing Y-ACCDIST to classify speakers based on language background and region.
  • Implementing Y-ACCDIST cluster analysis to group speakers and identify potential communities of practice.
  • Performing Y-ACCDIST feature selection to identify phonemes crucial for distinguishing speaker groups.

Main Results:

  • Y-ACCDIST efficiently and objectively assesses speaker similarities.
  • Cluster analysis reveals groupings consistent with localized social networks.
  • Feature selection identifies key phonemes for differentiating speaker groups.

Conclusions:

  • Y-ACCDIST serves as a valuable tool for preliminary screening and analysis of sociophonetic data.
  • The system's outputs can corroborate and enhance traditional sociophonetic analyses.
  • Y-ACCDIST facilitates the identification of linguistic variation and social networks within corpora.