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J Am Acad Audiol 2021; 32(09): 606-615
DOI: 10.1055/s-0041-1733964
Research Article

Automated Forced-Choice Tests of Speech Recognition

Robert H. Margolis
1   Audiology Incorporated, Arden Hills, Minnesota
2   Division of Speech and Hearing Science, College of Health Solutions, Arizona State University, Tempe, Arizona
,
Richard H. Wilson
2   Division of Speech and Hearing Science, College of Health Solutions, Arizona State University, Tempe, Arizona
,
George L. Saly
1   Audiology Incorporated, Arden Hills, Minnesota
,
Heather M. Gregoire
3   Childrens Hospital Colorado, Audiology Services, Colorado Springs, Colorado
,
Brandon M. Madsen
4   National Center for Rehabilitative Auditory Research, Portland, Oregon
› Author Affiliations Funding This work was supported by grant no. 1R41DC006509 from the National Institute on Deafness and Other Communication Disorders.
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Abstract

Purpose This project was undertaken to develop automated tests of speech recognition, including speech-recognition threshold (SRT) and word-recognition test, using forced-choice responses and computerized scoring of responses. Specific aims were (1) to develop an automated method for measuring SRT for spondaic words that produces scores that are in close agreement with average pure-tone thresholds and (2) to develop an automated test of word recognition that distinguishes listeners with normal hearing from those with sensorineural hearing loss and which informs the hearing aid evaluation process.

Method An automated SRT protocol was designed to converge on the lowest level at which the listener responds correctly to two out of two spondees presented monaurally. A word-recognition test was conducted with monosyllabic words (female speaker) presented monaurally at a fixed level. For each word, there were three rhyming foils, displayed on a touchscreen with the test word. The listeners touched the word they thought they heard. Participants were young listeners with normal hearing and listeners with sensorineural hearing loss. Words were also presented with nonrhyming foils and in an open-set paradigm. The open-set responses were scored by a graduate student research assistant.

Results The SRT results agreed closely with the pure-tone average (PTA) obtained by automated audiometry. The agreement was similar to results obtained with the conventional SRT scoring method. Word-recognition scores were highest for the closed-set, nonrhyming lists and lowest for open-set responses. For the hearing loss participants, the scores varied widely. There was a moderate correlation between word-recognition scores and pure-tone thresholds which increased as more high frequencies were brought into the PTA. Based on the findings of this study, a clinical protocol was designed that determines if a listener's performance was in the normal range and if the listener benefited from increasing the level of the stimuli.

Conclusion SRTs obtained using the automated procedure are comparable to the results obtained by the conventional clinical method that is in common use. The automated closed-set word-recognition test results show clear differentiation between scores for the normal and hearing loss groups. These procedures provide clinical test results that are not dependent on the availability of an audiologist to perform the tests.

Keywords

forced-choice - speech recognition - speech-recognition threshold

Note

This work was presented at the annual meeting of the American Auditory Society, March 4, 2016.


Disclaimer

Any mention of a product, service, or procedure in the Journal of the American Academy of Audiology does not constitute an endorsement of the product, service, or procedure by the American Academy of Audiology.


Supplementary Material



Publication History

Received: 09 February 2021

Accepted: 01 July 2021

Article published online:
17 February 2022

© 2022. American Academy of Audiology. This article is published by Thieme.

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  • References

  • 1 Mahomed F, Swanepoel W, Eikelboom RH, Soer M. Validity of automated threshold audiometry: a systematic review and meta-analysis. Ear Hear 2013; 34 (06) 745-752
    CrossrefPubMedSearch in Google Scholar
  • 2 Margolis RH, Glasberg BR, Creeke S, Moore BCJ. AMTAS: automated method for testing auditory sensitivity: validation studies. Int J Audiol 2010; 49 (03) 185-194
    CrossrefPubMedSearch in Google Scholar
  • 3 Margolis RH, Saly GL, Le C, Laurence J. Qualind: a method for assessing the accuracy of automated tests. J Am Acad Audiol 2007; 18 (01) 78-89
    Thieme ConnectPubMedSearch in Google Scholar
  • 4 Margolis RH. Automated speech recognition test. Presented to the American Auditory Society, Scottsdale, AZ; March 4, 2010
  • 5 Ooster J, Krueger M, Bach J-H, Wagener KC, Kollmeier B, Meyer BT. Speech audiometry at home: automated listening tests via smart speakers with normal-hearing and hearing-impaired listeners. Trends Hear 2020; 24 (04) 1-13
    Search in Google Scholar
  • 6 Black JW. Multiple-choice intelligibility tests. J Speech Hear Disord 1957; 22 (02) 213-235
    CrossrefPubMedSearch in Google Scholar
  • 7 House AS, Williams CE, Heker MH, Kryter KD. Articulation-testing methods: consonantal differentiation with a closed-response set. J Acoust Soc Am 1965; 37 (01) 158-166
    CrossrefPubMedSearch in Google Scholar
  • 8 Dewyer NA, Jiradejvong P, Henderson Sabes J, Limb CJ. Automated smartphone audiometry: Validation of a word recognition test app. Laryngoscope 2018; 128 (03) 707-712
    CrossrefPubMedSearch in Google Scholar
  • 9 McCullough JA, Cunningham LA, Wilson RH. Auditory-visual word identification test materials: computer application with children. J Am Acad Audiol 1992; 3 (03) 208-214
    PubMedSearch in Google Scholar
  • 10 McCullough JA, Wilson RH, Birck JD, Anderson LG. A multimedia approach for estimating speech recognition of multilingual clients. Am J Audiol 1994; 3 (01) 19-22
    CrossrefSearch in Google Scholar
  • 11 Clopper CG, Pisoni DB, Tierney AT. Effects of open-set and closed-set task demands on spoken word recognition. J Am Acad Audiol 2006; 17 (05) 331-349
    Thieme ConnectPubMedSearch in Google Scholar
  • 12 Margolis RH, Saly GL. Toward a standard description of hearing loss. Int J Audiol 2007; 46 (12) 746-758
    CrossrefPubMedSearch in Google Scholar
  • 13 Margolis RH, Saly GL. Distribution of hearing loss characteristics in a clinical population. Ear Hear 2008; 29 (04) 524-532
    CrossrefPubMedSearch in Google Scholar
  • 14 Fletcher H. A method of calculating hearing loss for speech from an audiogram. Acta Otolaryngol 1950; a; 38 (Suppl. 90) 26-37
    CrossrefPubMedSearch in Google Scholar
  • 15 Fletcher H. A method of calculating hearing loss for speech from an audiogram. J Acoust Soc Am 1950; b; 22 (01) 1-5
    CrossrefSearch in Google Scholar
  • 16 Hirsh IJ, Davis H, Silverman SR, Reynolds EG, Eldert E, Benson RW. Development of materials for speech audiometry. J Speech Hear Disord 1952; 17 (03) 321-337
    CrossrefPubMedSearch in Google Scholar
  • 17 Tillman TW, Carhart R. An expanded test for speech discrimination utilizing CNC monosyllabic words (Northwestern University Auditory Test No. 6, Technical Report No. SAM-TR-66–55). Brooks Air Force Base, TX: USAF School of Aerospace Medicine; 1966
    Search in Google Scholar
  • 18 Department of Veterans Affairs. Speech Recognition and Identification Materials. Disc 4.0. Mountain Home, TN: VA Medical Center; 2010
    Search in Google Scholar
  • 19 Wilson RH, McArdle R. The homogeneity with respect to intelligibility of recorded word-recognition materials. J Am Acad Audiol 2015; 26 (04) 331-345
    Thieme ConnectPubMedSearch in Google Scholar
  • 20 Margolis RH. A self-calibrating audiometer. Presented to the American Auditory Society, Scottsdale, AZ; March 8, 2012
  • 21 American National Standards Institute. ANSI S3.6–2018. Specification for Audiometers. New York, NY: American National Standards Institute; 2018
    Search in Google Scholar
  • 22 Harris JD, Haines HL, Myers CK. A new formula for using the audiogram to predict speech hearing loss. AMA Arch Otolaryngol 1956; 63 (02) 158-176
    CrossrefPubMedSearch in Google Scholar
  • 23 Delk JH, Glorig A, Quiggle RR, Summerfield AB. Predicting hearing loss for speech from pure tone audiograms. Laryngoscope 1957; 67 (01) 1-15
    CrossrefPubMedSearch in Google Scholar
  • 24 Wilson RH, Margolis RH. Measurements of auditory thresholds for speech stimuli. In: Konkle DF, Rintelmann WF. eds. Principles of Speech Audiometry. Baltimore, MD: University Park Press; 1983: 79-126
    Search in Google Scholar
  • 25 Siegenthaler BM, Strand R. Audiogram-average methods and SRT scores. J Acoust Soc Am 1964; 36 (03) 589-593
    CrossrefSearch in Google Scholar
  • 26 Carhart R. Observations on relations between thresholds for pure tones and for speech. J Speech Hear Disord 1971; 36 (04) 476-483
    CrossrefPubMedSearch in Google Scholar
  • 27 Wilson RH, Antablin JK. A picture identification task as an estimate of the word-recognition performance of nonverbal adults. J Speech Hear Disord 1980; 45 (02) 223-238
    CrossrefPubMedSearch in Google Scholar
  • 28 Wilson RH, Zizz CA, Shanks JE, Causey GD. Normative data in quiet, broadband noise, and competing message for Northwestern University Auditory Test No. 6 by a female speaker. J Speech Hear Disord 1990; 55 (04) 771-778
    CrossrefPubMedSearch in Google Scholar
  • 29 Wilson RH, Antablin JK. The picture identification task, a reply to Dillon. J Speech Hear Disord 1982; 47 (01) 111-112
    CrossrefSearch in Google Scholar
  • 30 Wilson RH. Clinical experience with the words-in-noise test on 3430 veterans: comparisons with pure-tone thresholds and word recognition in quiet. J Am Acad Audiol 2011; 22 (07) 405-423
    Thieme ConnectPubMedSearch in Google Scholar
  • 31 Margolis RH, Millin JP. An item-difficulty based speech discrimination test. J Speech Hear Res 1971; 14 (04) 865-873
    CrossrefPubMedSearch in Google Scholar
  • 32 Massachusetts Eye and Ear Infirmary. Q/MASS Speech Audiometry, Vol. 3. Boston MA: Massachusetts Eye and Ear Infirmary; 1991
    Search in Google Scholar
  • 33 Studebaker GA, Sherbecoe RL. Frequency-importance and transfer functions for recorded CID W-22 word lists. J Speech Hear Res 1991; 34 (02) 427-438
    CrossrefPubMedSearch in Google Scholar
  • 34 Plomp R. Auditory handicap of hearing impairment and the limited benefit of hearing aids. J Acoust Soc Am 1978; 63 (02) 533-549
    CrossrefPubMedSearch in Google Scholar
  • 35 Carhart R. Basic principles of speech audiometry. Acta Otolaryngol 1951; 40 (1-2): 62-71
    CrossrefPubMedSearch in Google Scholar