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Heather L. Lohmeier

Researcher at University of Wisconsin-Madison

Publications -  8
Citations -  615

Heather L. Lohmeier is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Motor speech disorders & Phonetics. The author has an hindex of 7, co-authored 8 publications receiving 526 citations. Previous affiliations of Heather L. Lohmeier include University of Arizona.

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Journal ArticleDOI

Extensions to the Speech Disorders Classification System (SDCS)

TL;DR: Examples of research using the extensions to the SDCS described in the present report include diagnostic findings for a sample of youth with motor speech disorders associated with galactosemia, and a test of the hypothesis of apraxia of speech in a group of children with autism spectrum disorders.
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Encoding, Memory, and Transcoding Deficits in Childhood Apraxia of Speech

TL;DR: Speakers with CAS have speech processing deficits in encoding, memory, and transcoding, according to the Syllable Repetition Task (SRT).
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A Nonword Repetition Task for Speakers With Misarticulations: The Syllable Repetition Task (SRT)

TL;DR: The SRT appears to be a psychometrically stable and substantively informative nonword repetition task for emerging genetic research and other research with speakers who misarticulate and both memorial and auditory-perceptual encoding constraints underlying non word repetition errors in children with speech-language impairment.
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Clinical ventilator adjustments that improve speech.

TL;DR: These simple interventions markedly improve ventilator-supported speech and are safe, at least when used on a short-term basis, and high PEEP is a safer alternative than a one-way valve.
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Perceptual and acoustic reliability estimates for the Speech Disorders Classification System (SDCS)

TL;DR: In this article, a companion paper describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS), which uses perceptual and acoustic data reduction methods to obtain information on a speaker's speech, prosody, and voice.