Evidence map›Paper›PMID 42600158›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2026

Effect of Speaking Rate and Sentence Context on Comprehension and Recognition Memory of Degraded Speech.

Tirth R Patel, Victoria A Sevich, Xiaoke Feng, Liping Du, Terrin N Tamati

Abstract read
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Tirth R PatelDepartment of Otolaryngology-Head and Neck Surgery, University of Michigan Health System, Ann Arbor.
Victoria A SevichDepartment of Speech and Hearing Science, The Ohio State University, Columbus.ORCID 0000-0003-0616-448X
Xiaoke FengDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN.
Liping DuDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN.
Terrin N TamatiDepartment of Speech and Hearing Science, The Ohio State University, Columbus.ORCID 0000-0003-1885-1877

Funding

Beyond Clinical Measures: Auditory-Social Experience and Robust Communication Skills in Adults Following Cochlear ImplantationR21DC019382 · NIDCD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI TAMATI, TERRIN NICHOLE · 2022 to 2024
$634k
NIDCD NIH HHS R21 DC019382
6 · The paper itself

Abstract

purposeVariability in speaking rate and sentence context affects the cognitive processing of speech. Individuals relying on a degraded speech signal may be particularly susceptible to these effects. In this study, we examine how artificial alterations to speaking rates and sentence context affect sentence comprehension and recognition memory for cochlear implant (CI) users and normal-hearing (NH) adults presented with spectrally degraded speech.

methodThirty-seven adult CI users and 71 NH adults participated in the study. Participants completed a sentence verification task paired with a recognition memory task consisting of sentences with artificially manipulated speaking rates and variable sentence context. The sentences presented to NH adults were vocoder processed to simulate CI hearing. Accuracy and response times (RTs) from both the sentence verification task and the sentence recognition memory task were assessed. Logistic and ordinal regression models were constructed to assess the effects of speaking rate and sentence context on the participants' performance.

resultsOn the sentence verification task, slow sentences had a greater accuracy of sentence comprehension and faster RTs compared to fast sentences, particularly more so for low-context sentences. On the recognition memory task, slow sentences had a greater accuracy of sentence recognition and faster RTs compared to fast sentences. However, the effect of context was less pronounced and actually favored low-context sentences. CI users and older listeners, regardless of hearing status, showed lower overall accuracy and slower RTs.

conclusionsSpeaking rate and sentence context impact sentence comprehension and recognition memory for spectrally degraded speech. Slower speech facilitates processing, especially when contextual support is limited. CI users and older listeners showed slower and more effortful processing. These findings may highlight the importance of speaking rate in effective communication strategies and support theoretical approaches linking increased cognitive effort to the perception of spectrally degraded speech. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33179354.

Indexed as

ComprehensionRecognition, PsychologySpeechSpeech PerceptionAdultAgedCochlear ImplantsFemaleHumansMaleMemoryMiddle AgedReaction TimeYoung Adult

Identifiers

PMID42600158
PMCPMC13577953

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.