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Related Experiment Video

Updated: Jul 4, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Characterizing correlations in partial credit speech recognition scoring with beta-binomial distributions.

Adam K Bosen1

  • 1Center for Hearing Research, Boys Town National Research Hospital, Omaha, Nebraska 68131, USAadam.bosen@boystown.org.

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|February 1, 2024
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Summary

Partial credit scoring enhances speech recognition precision. However, contextual cues create correlations in accuracy, which must be addressed in statistical power analyses for cochlear implant users.

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Area of Science:

  • Auditory neuroscience
  • Speech processing
  • Statistical modeling

Background:

  • Partial credit scoring can improve measurement precision in speech recognition.
  • Assessing this improvement is complex due to contextual cues creating correlations in token identification probabilities.

Purpose of the Study:

  • To estimate speech recognition accuracy and intraclass correlation using beta-binomial distributions.
  • To investigate these correlations in phonemes within words and words within sentences for cochlear implant listeners.

Main Methods:

  • Utilized beta-binomial distributions to model recognition accuracy.
  • Analyzed phoneme and word recognition in 20 cochlear implant users.
  • Calculated intraclass correlation coefficients for recognition accuracy within stimuli.

Main Results:

  • Demonstrated substantial intraclass correlation in recognition accuracy within stimuli.
  • Found these correlations to be invariant across individual participants.
  • Highlighted the impact of contextual cues on speech recognition data.

Conclusions:

  • Intraclass correlations in speech recognition accuracy are significant and consistent across individuals.
  • These correlations necessitate consideration in power analyses for partial credit scoring.
  • Findings are crucial for accurate measurement in cochlear implant research.