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Automated Speech Audiometry: Can It Work Using Open-Source Pre-Trained Kaldi-NL Automatic Speech Recognition?

Gloria Araiza-Illan1,2, Luke Meyer1,2, Khiet P Truong3

  • 1Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Trends in Hearing
|March 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated digits-in-noise (DIN) hearing screening test using Kaldi-NL. The automated system accurately assesses spoken responses, showing potential for clinical use in hearing evaluations.

Keywords:
automatic speech recognitiondigits-in-noise testspeech audiometryspeech perceptionspeech-in-noise hearing test

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Area of Science:

  • Audiology
  • Speech Processing
  • Computational Linguistics

Background:

  • The digits-in-noise (DIN) test is a valuable tool for hearing screening across diverse populations.
  • Current DIN test administration relies on human supervisors or manual response entry.
  • Automating the DIN test can enhance efficiency and accessibility in hearing assessments.

Purpose of the Study:

  • To develop and evaluate an automated digits-in-noise (DIN) test system using the Kaldi-NL toolkit for spoken response evaluation.
  • To assess the performance of the Kaldi-NL system in accurately transcribing spoken digits in noise.
  • To determine the impact of automated transcription errors on the speech reception threshold (SRT) output.

Main Methods:

  • An automated DIN test was developed utilizing the open-source Kaldi-NL automatic speech recognition toolkit.
  • Thirty self-reported normal-hearing Dutch adults participated in the study.
  • The system evaluated spoken responses, and its performance was measured by word error rate (WER) and its effect on SRT via bootstrapping simulations.

Main Results:

  • The Kaldi-NL system demonstrated an average word error rate (WER) of 5.0% across participants.
  • An average of three triplets per participant contained decoding errors.
  • Simulations indicated that up to four triplets with decoding errors minimally impacted the speech reception threshold (SRT), remaining within typical variability.

Conclusions:

  • The proposed automated DIN test setup using Kaldi-NL is feasible for clinical applications.
  • The system shows promise for unsupervised hearing screening and assessment.
  • Further validation may confirm its utility in real-world audiological settings.