THE RELATIONSHIP BETWEEN SCAN:3-A AND HEARING-IN-NOISE TEST PERFORMANCES

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Keerthana Velappan (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: "According to the Central Auditory Processing Disorder (CAPD) guidelines by the American Academy of Audiology (AAA; 2010) , ""specific treatment options (e.g. personal FM systems) may be more appropriately recommended for individuals who present deficits on monaural low-redundancy (MLR) (e.g. speech recognition in noise , filtered or compressed speech) and/or dichotic speech tests."" The assumption appears to be that MLR speech tests may be used to adequately evaluate the necessity for an FM system. In other words , the MLR speech tests may be used to determine the presence of a speech recognition in noise deficit. The purpose of this study was to evaluate the assumption that ""low-redundancy€ speech tests may be used to identify the need for an FM system by comparing these test results to that of the Hearing-in-Noise Test (HINT; Nilsson et al. , 1994; Vermiglio , 2008). Twenty-nine young , native English-speaking with normal pure tone thresholds participated in the study. The subjects were evaluated using the MLR subtests of the SCAN-3:A and the HINT. The SCAN-3:A subtests included: Auditory Figure-Ground (0 dB) , Filtered Words , Competing Words-Directed Ear , Competing Sentences , and Time-Compressed Sentences. The standard HINT conditions included; Quiet , Noise Front , Noise Right , and Noise Left. Composite scores were determined for each test battery. Statistically significant relationships were found between most of the SCAN-3:A conditions and HINT conditions. However , while relatively strong relationships between the MLR and HINT test results were found , clinicians should be cautious when inferring the presence of an SRN disorder from the MLR test results."

Additional Information

Publication

Thesis

Language: English

Date: 2017

Keywords

SCAN, speech recognition in noise ability, HINT

Subjects

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