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A Novel Computerized Approach to Constructing Speech Audiometry Materials: Development of a Perceptually Balanced Hebrew Word Recognition Test.

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Title: A Novel Computerized Approach to Constructing Speech Audiometry Materials: Development of a Perceptually Balanced Hebrew Word Recognition Test.
Authors: Horev, Nitza1,2 Nitzahorev@gmail.com, Putter-Katz, Hanna1
Source: Journal of Speech, Language & Hearing Research. Jul2026, Vol. 69 Issue 7, p3417-3436. 20p.
Subject Terms: *Language & languages, *Computers, *Data analysis, *Research methodology evaluation, *Consonants, *Experimental design, *Speech audiometry, *Research methodology, *Speech perception, *Algorithms, Noise control, Pearson correlation (Statistics), Vowels, T-test (Statistics), Questionnaires, Signal processing, Analysis of variance, Statistics, One-way analysis of variance, Hearing levels, Calibration, Data analysis software, Phonetics, Transducers, Regression analysis
Abstract: Purpose: The purpose of this study was to introduce and validate a novel computerized approach for constructing equivalent word lists for speech recognition testing and to demonstrate this methodology through the development of consonant–vowel–consonant (CVC) word lists in Hebrew. Method: The study was conducted in three phases. Phase 1 empirically quantified word difficulty (50% recognition threshold in noise) for 275 Hebrew CVC words among 60 normal-hearing listeners. Phase 2 utilized a custom Python optimization algorithm to allocate 200 words into eight 25-word lists and four 50-word sets. The algorithm simultaneously balanced multiple variables, including perceptual difficulty (50% point), phonemic distribution, and word familiarity. Phase 3 empirically validated the lists’ equivalency for speech recognition in quiet among 120 normal-hearing listeners by establishing performance–intensity functions. Results: The optimization algorithm successfully generated balanced lists. Validation analyses of speech recognition scores in quiet (analyses of variances) demonstrated robust interlist equivalency; no statistically significant differences were found among the 25-word lists or 50-word sets at any of the presentation levels tested. The lists exhibited homogeneous psychometric functions (e.g., mean slope of 4.64%/dB for 50-word sets) consistent with international standards. Conclusions: A comprehensive set of Hebrew speech recognition test materials was successfully developed and validated. The novel methodology, integrating empirical difficulty measurement with computational optimization, proved effective. This approach provides a systematic, objective, and replicable model for developing standardized speech recognition tests across diverse languages. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: A Novel Computerized Approach to Constructing Speech Audiometry Materials: Development of a Perceptually Balanced Hebrew Word Recognition Test.
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  Data: <searchLink fieldCode="AR" term="%22Horev%2C+Nitza%22">Horev, Nitza</searchLink><relatesTo>1,2</relatesTo><i> Nitzahorev@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Putter-Katz%2C+Hanna%22">Putter-Katz, Hanna</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Speech%2C+Language+%26+Hearing+Research%22">Journal of Speech, Language & Hearing Research</searchLink>. Jul2026, Vol. 69 Issue 7, p3417-3436. 20p.
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  Data: *<searchLink fieldCode="DE" term="%22Language+%26+languages%22">Language & languages</searchLink><br />*<searchLink fieldCode="DE" term="%22Computers%22">Computers</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Research+methodology+evaluation%22">Research methodology evaluation</searchLink><br />*<searchLink fieldCode="DE" term="%22Consonants%22">Consonants</searchLink><br />*<searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+audiometry%22">Speech audiometry</searchLink><br />*<searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+perception%22">Speech perception</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Noise+control%22">Noise control</searchLink><br /><searchLink fieldCode="DE" term="%22Pearson+correlation+%28Statistics%29%22">Pearson correlation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Vowels%22">Vowels</searchLink><br /><searchLink fieldCode="DE" term="%22T-test+%28Statistics%29%22">T-test (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22One-way+analysis+of+variance%22">One-way analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Hearing+levels%22">Hearing levels</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Phonetics%22">Phonetics</searchLink><br /><searchLink fieldCode="DE" term="%22Transducers%22">Transducers</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: The purpose of this study was to introduce and validate a novel computerized approach for constructing equivalent word lists for speech recognition testing and to demonstrate this methodology through the development of consonant–vowel–consonant (CVC) word lists in Hebrew. Method: The study was conducted in three phases. Phase 1 empirically quantified word difficulty (50% recognition threshold in noise) for 275 Hebrew CVC words among 60 normal-hearing listeners. Phase 2 utilized a custom Python optimization algorithm to allocate 200 words into eight 25-word lists and four 50-word sets. The algorithm simultaneously balanced multiple variables, including perceptual difficulty (50% point), phonemic distribution, and word familiarity. Phase 3 empirically validated the lists’ equivalency for speech recognition in quiet among 120 normal-hearing listeners by establishing performance–intensity functions. Results: The optimization algorithm successfully generated balanced lists. Validation analyses of speech recognition scores in quiet (analyses of variances) demonstrated robust interlist equivalency; no statistically significant differences were found among the 25-word lists or 50-word sets at any of the presentation levels tested. The lists exhibited homogeneous psychometric functions (e.g., mean slope of 4.64%/dB for 50-word sets) consistent with international standards. Conclusions: A comprehensive set of Hebrew speech recognition test materials was successfully developed and validated. The novel methodology, integrating empirical difficulty measurement with computational optimization, proved effective. This approach provides a systematic, objective, and replicable model for developing standardized speech recognition tests across diverse languages. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1044/2026_JSLHR-25-00933
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 3417
    Subjects:
      – SubjectFull: Language & languages
        Type: general
      – SubjectFull: Computers
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Research methodology evaluation
        Type: general
      – SubjectFull: Consonants
        Type: general
      – SubjectFull: Experimental design
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      – SubjectFull: Speech audiometry
        Type: general
      – SubjectFull: Research methodology
        Type: general
      – SubjectFull: Speech perception
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Noise control
        Type: general
      – SubjectFull: Pearson correlation (Statistics)
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      – SubjectFull: Vowels
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      – SubjectFull: T-test (Statistics)
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      – SubjectFull: Statistics
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      – SubjectFull: One-way analysis of variance
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      – SubjectFull: Hearing levels
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Phonetics
        Type: general
      – SubjectFull: Transducers
        Type: general
      – SubjectFull: Regression analysis
        Type: general
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      – TitleFull: A Novel Computerized Approach to Constructing Speech Audiometry Materials: Development of a Perceptually Balanced Hebrew Word Recognition Test.
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            NameFull: Horev, Nitza
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              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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