About

Rosemary A. Lester-Smith, Ph.D., CCC-SLP, is an Associate Professor in the Department of Speech, Language, and Hearing Sciences at The University of Texas at Austin and the Director of the UT Voice Lab. She received a B.A. in Speech and Hearing Sciences from the University of New Mexico, an M.A. in Speech and Hearing Sciences from Indiana University, an M.S. in Clinical Investigation from Northwestern University, and a Ph.D. in Speech, Language, and Hearing Sciences with a minor in Neuroscience from the University of Arizona. She completed postdoctoral training at Mayo Clinic, Boston University, Northwestern University, and Shirley Ryan AbilityLab (formerly Rehabilitation Institute of Chicago). She is a certified speech-language pathologist and has worked in various clinical settings, primarily evaluating and treating adults with voice and swallowing disorders. Dr. Lester-Smith’s research focuses on improving the diagnosis and treatment of neurogenic voice disorders, including vocal tremor. She studies voice production in speakers with neurological disorders, healthy speakers, and singers to understand factors that impair or enhance vocal control. Her research employs acoustical, perceptual, and physiological methods, and she utilizes computational models to simulate vocal tremor and vibrato, perturbation of voice auditory feedback to study auditory-motor control, and single-case experimental designs to evaluate voice therapy effectiveness. Her work is funded by the National Institutes of Health.

Research topics

• Computer Science
• Psychology
• Audiology
• Medicine
• Physics
• Artificial Intelligence
• Speech recognition
• Neuroscience
• Acoustics
• Communication
• Psychiatry
• Cognitive psychology

Selected publications

• A - 53 A Digital Health Solution for Early Detection of Cognitive Impairment in Primary Care

  Archives of Clinical Neuropsychology · 2023

  Senior authorCorresponding
  • Psychology
  • Cognitive psychology
  • Audiology

  Abstract Objective To determine which task or combination of tasks provided the most effective way to differentiate cognitively impaired (CI) from cognitively normal (CN) participants in under 5 minutes and to ensure that classification accuracy was equal to or better than a traditional brief cognitive screening task, the Quick Mild Cognitive Impairment (Qmci) screen. Method CN (n = 53) and CI (n = 51) participants completed a risk assessment task, a symbol matching (SM) task, and four speech-language tasks, followed by a second administration of SM to examine utility of practice effects administered on an iPad. Participants also completed the Qmci. Eleven models were tested using Bayesian adaptive regression trees. Results The top three models all included the two SM variables: the one with SM by itself (estimated c = 0.91), one with SM and features from a personal narrative task (c = 0.94), and one with SM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. For comparison, the QMCI-only model yielded c = 0.91. Conclusions A combination of working memory/processing speed and acoustic and linguistic variables from recalling a personal story achieved a high level of classification accuracy, slightly exceeding that of a traditional cognitive screening task. The inclusion of both verbal and nonverbal tasks may be an important feature, allowing for cognitive screening of individuals who are not able to do one type of task or the other. Future work is planned to examine this shortened tool in a pragmatic clinical trial in two primary care clinics.

  DOI

• The Relation of Articulatory and Vocal Auditory–Motor Control in Typical Speakers

  Journal of Speech Language and Hearing Research · 2020 · 44 citations

  1st authorCorresponding
  • Computer Science
  • Psychology
  • Audiology

  . Conclusion These findings indicate that there may be disparate feedback and feedforward control mechanisms for articulatory and vocal error correction based on auditory feedback.

  DOI

• The Effect of Pitch Auditory Feedback Perturbations on the Production of Anticipatory Phrasal Prominence and Boundary

  Journal of Speech Language and Hearing Research · 2020 · 9 citations

  • Computer Science
  • Artificial Intelligence
  • Speech recognition

  Purpose In this study, we investigated how the direction and timing of a perturbation in voice pitch auditory feedback during phrasal production modulated the magnitude and latency of the pitch-shift reflex as well as the scaling of acoustic production of anticipatory intonation targets for phrasal prominence and boundary. Method Brief pitch auditory feedback perturbations (±200 cents for 200-ms duration) were applied during the production of a target phrase on the first or the second word of the phrase. To replicate previous work, we first measured the magnitude and latency of the pitch-shift reflex as a function of the direction and timing of the perturbation within the phrase. As a novel approach, we also measured the adjustment in the production of the phrase-final prominent word as a function of perturbation direction and timing by extracting the acoustic correlates of pitch, loudness, and duration. Results The pitch-shift reflex was greater in magnitude after perturbations on the first word of the phrase, replicating the results from Mandarin speakers in an American English-speaking population. Additionally, the production of the phrase-final prominent word was acoustically enhanced (lengthened vowel duration and increased intensity and fundamental frequency) after perturbations earlier in the phrase, but more so after perturbations on the first word in the phrase. Conclusion The timing of the pitch perturbation within the phrase modulated both the magnitude of the pitch-shift reflex and the production of the prominent word, supporting our hypothesis that speakers use auditory feedback to correct for immediate production errors and to scale anticipatory intonation targets during phrasal production.

  DOI

Recent grants

• NIH Grant F31DC012697

  NIH · $83k · 2014

• Auditory-Motor Control of Voice in Individuals with Essential Vocal Tremor

  NIH · $447k · 2019–2023

Frequent coauthors

• Ashling A. Lupiani

  Boston University

  13 shared

• Ayoub Daliri

  Arizona State University

  13 shared

• Cara E. Stepp

  University of Massachusetts Boston

  10 shared

• Defne Abur

  10 shared

• Allison Hilger

  University of Colorado Boulder

  9 shared

• Monique Tardif

  Université du Québec à Montréal

  9 shared

• Nicole M. Enos

  Boston University

  8 shared

• Charles R. Larson

  Northwestern University

  8 shared

Education

• Master of Science, Clinical Investigation

  Northwestern University

  2019

• Doctor of Philosophy, Speech, Language, and Hearing Sciences

  University of Arizona

  2014

• Master of Arts, Speech and Hearing Sciences

  Indiana University

  2007

• Bachelor of Arts, Speech and Hearing Sciences

  University of New Mexico

  2004

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