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Feasibility of an Adaptive Version of the Everyday Conversational Sentences in Noise Test.

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Title: Feasibility of an Adaptive Version of the Everyday Conversational Sentences in Noise Test.
Authors: Miles, Kelly1 kelly.miles@mq.edu.au, Best, Virginia2, Buchholz, Jörg M.1
Source: Journal of Speech, Language & Hearing Research. Feb2024, Vol. 67 Issue 2, p680-687. 8p.
Subject Terms: *Computer adaptive testing, *Research, *Hearing, *Conversation, *Speech audiometry, *Evaluation research, *Comparative studies, *Hearing disorders, *Listening, *Statistical correlation, Statistical reliability, Noise, Audiology, Psychometrics, Descriptive statistics, Research funding, Sound, Speech
Geographic Terms: Australia
Abstract: Purpose: To investigate potential reasons for the mismatch between laboratory/ clinic-based sentence-in-noise performance and real-world listening abilities, we recently developed a corpus of natural, spontaneously spoken speech with three vocal effort levels (Everyday Conversational Sentences in Noise [ECOSiN]). Here, we examined the feasibility of using the ECO-SiN corpus for adaptive speech-in-noise testing, which might be a desirable format in certain situations (e.g., during a clinical visit). Method: Ten young, normal-hearing adults, along with 20 older adults with hearing loss participated in the study. Speech reception thresholds (SRTs) were obtained using ECO-SiN sentences, which were systematically compared to the SRTs obtained using traditional Bamford-Kowal-Bench-like sentences. Results: Results demonstrated the properties of the test compared favorably with those of a standard test based on scripted and clearly spoken sentences. Moreover, whereas normal-hearing listeners received a benefit from an increase in vocal effort, the participants with hearing loss showed a disbenefit that increased with increasing hearing loss. Conclusion: The adaptive version of the ECO-SiN test is feasible for research and clinical testing. [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: Feasibility of an Adaptive Version of the Everyday Conversational Sentences in Noise Test.
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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>. Feb2024, Vol. 67 Issue 2, p680-687. 8p.
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  Data: Purpose: To investigate potential reasons for the mismatch between laboratory/ clinic-based sentence-in-noise performance and real-world listening abilities, we recently developed a corpus of natural, spontaneously spoken speech with three vocal effort levels (Everyday Conversational Sentences in Noise [ECOSiN]). Here, we examined the feasibility of using the ECO-SiN corpus for adaptive speech-in-noise testing, which might be a desirable format in certain situations (e.g., during a clinical visit). Method: Ten young, normal-hearing adults, along with 20 older adults with hearing loss participated in the study. Speech reception thresholds (SRTs) were obtained using ECO-SiN sentences, which were systematically compared to the SRTs obtained using traditional Bamford-Kowal-Bench-like sentences. Results: Results demonstrated the properties of the test compared favorably with those of a standard test based on scripted and clearly spoken sentences. Moreover, whereas normal-hearing listeners received a benefit from an increase in vocal effort, the participants with hearing loss showed a disbenefit that increased with increasing hearing loss. Conclusion: The adaptive version of the ECO-SiN test is feasible for research and clinical testing. [ABSTRACT FROM AUTHOR]
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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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=175404838
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        Value: 10.1044/2023_JSLHR-23-00507
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        Text: English
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        StartPage: 680
    Subjects:
      – SubjectFull: Computer adaptive testing
        Type: general
      – SubjectFull: Research
        Type: general
      – SubjectFull: Hearing
        Type: general
      – SubjectFull: Conversation
        Type: general
      – SubjectFull: Speech audiometry
        Type: general
      – SubjectFull: Evaluation research
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Hearing disorders
        Type: general
      – SubjectFull: Listening
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      – SubjectFull: Statistical correlation
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      – SubjectFull: Statistical reliability
        Type: general
      – SubjectFull: Noise
        Type: general
      – SubjectFull: Audiology
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      – SubjectFull: Psychometrics
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Research funding
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      – SubjectFull: Sound
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      – SubjectFull: Speech
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      – SubjectFull: Australia
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    Titles:
      – TitleFull: Feasibility of an Adaptive Version of the Everyday Conversational Sentences in Noise Test.
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            NameFull: Best, Virginia
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            NameFull: Buchholz, Jörg M.
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              M: 02
              Text: Feb2024
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