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Development and validation of KBio sentence lists.

Hemanth Narayan Shetty1, Monika Nagendra1

  • 1Department of Audiology, JSS Institute of Speech and Hearing, Mysuru, India.

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Summary

The KBio sentence lists effectively assess speech perception in children with cochlear implants (CI). Performance is influenced by background noise, with lower Signal-to-Noise Ratios (SNRs) impacting recognition accuracy.

Keywords:
Kannadacontent validationsignal to noise ratiospeech intelligibility

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Area of Science:

  • Audiology
  • Speech-Language Pathology
  • Biomedical Engineering

Background:

  • Assessing speech perception in children with cochlear implants (CI) is crucial for evaluating device efficacy.
  • Existing assessment tools may require further validation for this specific population.
  • Understanding the impact of auditory noise on speech recognition is vital for CI users.

Purpose of the Study:

  • To develop and validate the KBio sentence lists for speech perception assessment in children with cochlear implants (CI).
  • To ensure the KBio lists possess high content validity and inter-rater reliability.
  • To evaluate the equivalency of the KBio lists across different Signal-to-Noise Ratios (SNRs).

Main Methods:

  • 301 sentences were rated by teachers, with 172 selected based on content validity.
  • Intelligibility was measured in normal-hearing children using a CI simulation.
  • 115 sentences were organized into five equivalent lists and tested in normal-hearing and CI users across varying SNRs.

Main Results:

  • The KBio lists demonstrated high content validity (Item Content Validity Index, Universal Agreement, Kappa coefficients).
  • Intelligibility scores ranged from 26% to 100%, with no significant differences between lists at each SNR.
  • CI users showed significant effects of SNR on speech recognition, with performance improving at higher SNRs.

Conclusions:

  • The KBio lists are validated tools for assessing speech perception in children with CI.
  • The lists exhibit strong validity, inter-rater agreement, and equivalency after CI simulation.
  • Speech recognition accuracy is significantly affected by background noise levels (SNRs) in CI users.