My Google Scholar profile can be found here: Scholar Profile

My research program is situated in the domain of computational neuroscience, and centers on building computational models that are useful for making sense of human behavior. Human behavior is increasingly captured in quantitative ways, ranging from coding of discrete responses (e.g., "yes" vs. "no"), to signals that capture continuous dynamics (e.g., motion capture). Computational models have become a central tool for making sense of behavioral data, owing to their ability to link behavioral variables to underlying cognitive mechanisms, important neural correlates, and hidden patterns of behavior. I have a particular interest in models that link behaviors to the brain and brain health, and in the behaviors surrounding speech, language and hearing. Some examples of my research contributions are as follows:

Behavioral Assessment of Perception: I have developed and applied a variety of novel behavioral methodologies targeting auditory perception, including for tinnitus, the perception of pitch and prosodic emphasis in speech, and mechanisms of auditory attention. I have also worked to develop virtual reality-based assessments to identify multisensory (i.e., vestibular, visual and somatosensory) deficit phenotypes following neurotrauma.

Divenyi, P., & Lammert, A. (2007). The time course of listening bands. In Hearing - From sensory processing to perception, B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.). Berlin, Heidelberg (Germany): Springer Verlag.

Hovy, D., Anumanchipalli, G.K., Parlikar, A., Vaughn, C., Lammert, A., Hovy, E. & Black, A. (2013). Analysis and Modeling of "Focus" in Context. In INTERSPEECH-2013.

Rao, H.M., Talkar, T., Ciccarelli, G.A., Nolan, M., O'Brien, A., Vergara-Diaz, G., Sherrill, D., Zafonte, R., Palmer, J.S., Quatieri, T.F., McKindles, R.J., Bonato, P., & Lammert, A.C. (2020). Sensorimotor Conflict Tests in an Immersive Virtual Environment Reveal Subclinical Impairments in Mild Traumatic Brain Injury. Scientific Reports.

Hoyland, A., Barnett, N. V., Roop, B. W., Alexandrou, D., Caplan, M., Mills, J., Parrell, B., Chari, D.A. & Lammert, A.C. (2023). Reverse Correlation Uncovers More Complete Tinnitus Spectra. IEEE Open Journal of Engineering in Medicine and Biology.

Enhanced Behavioral Methods: I have been engaged in enhancing the behavioral methods and clinical instruments used for assessment of speech and hearing. Instruments of interest include improved stimuli for eliciting the full range of relevant behaviors, as well as tools for accurately capturing behaviors in challenging or uncontrolled environments.

Lammert, A.C., Melot, J., Sturim, D.E., Hannon, D.J., DeLaura, R., Williamson, J.R., Ciccarelli, G. & Quatieri, T.F. (2020). Analysis of Phonetic Balance in Standard English Passages. Journal of Speech, Language and Hearing Research, 63(4), 917-930.

Ditthapron, A., Agu, E. & Lammert, A.C. (2021). Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Assessment of Speech Impairments. IEEE Open Journal of Engineering in Biology and Medicine.

Compton, A., Roop, B. W., Parrell, B., & Lammert, A. C. (2022). Stimulus Whitening Improves the Efficiency of Reverse Correlation. Behavior Research Methods 1-9.

Roop, B. W., Parrell, B., & Lammert, A. C. (2023). A Compressive Sensing Approach to Inferring Cognitive Representations with Reverse Correlation. bioRxiv, in revision at Behavior Research Methods.

Behavioral Assessment of Cognition: I have been developing behavioral biomarkers based upon novel behavioral measures, as well as deep learning-based classifiers. These methods have been used to successfully assess cognitive/neurological function and clinical diagnoses a variety of conditions, such as depression, cognitive fatigue, and traumatic brain injury using speech signals collected in laboratory and field settings.

Lee, C. C., Black, M., Katsamanis, A., Lammert, A. C., Baucom, B. R., Christensen, A. & Narayanan, S. S. (2010). Quantification of prosodic entrainment in affective spontaneous spoken interactions of married couples. In Eleventh Annual Conference of the International Speech Communication Association.

Rao, H.M., Smalt, C.J., Rodriguez, A., Wright, H., Mehta, D.D., Brattain, L.J., Edwards, H., Lammert, A., Heaton, K.J., Quatieri, T.F. (2020). Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task. Frontiers in Human Neuroscience, 14.

Seneviratne, N., Williamson, J.R., Lammert, A.C., Quatieri, T.F. & Espy-Wilson, C. (2020). Extended Study on the Use of Vocal Tract Variables to Quantify Neuromotor Coordination in Depression. INTERSPEECH-2020.

Heaton, K., Williamson, J., Lammert, A.C., Finkelstein, K., Haven, C., Sturim, D., Smalt, C. & Quatieri, T. (2020). Predicting Changes in Performance Due to Cognitive Fatigue: A Multimodal Approach Based on Speech Motor Coordination and Electrodermal Activity. The Clinical Neuropsychologist, 1-25.

Behavioral Variability: I have been working to gain fresh insights into longstanding challenges motor control during speech production, many of which center on understanding the wide behavioral variability exhibited across speakers. I have been using real-time MRI to capture the exquisitely controlled movements of the speech articulators in conjunction with their physical structure to explore key scientific questions regarding behavioral variability. Specifically, I have been working to expand our growing evidence that much of this variability can be explained by characterizing, amongst other elements, the physical structure of the speech production apparatus (i.e., its shape, size, etc.) and its interaction with motor planning and control.

Lammert, A., Proctor, M. & Narayanan, S. (2013). Interspeaker Variability in Hard Palate Morphology and Vowel Production. Journal of Speech, Language and Hearing Research, 56: S1924-S1933.

Lammert, A., Ramanarayanan, V., Goldstein, L. & Narayanan, S. (2014). Gestural Control in the English Past-Tense Suffix: an Articulatory Study using Real-Time MRI. Phonetica, 71(4): 229-248.

Lammert, A., & Narayanan, S. (2015). On Short-Time Estimation of Vocal Tract Length from Formant Frequencies. PLoS ONE 10(7), e0132193.

Lynn, E., Narayanan, S. S., & Lammert, A. C. (2021). Dark tone quality and vocal tract shaping in soprano song production: Insights from real-time MRI. JASA Express Letters, 1(7), 075202.

Cognitive Mechanisms of Behavior: My investigations into the sources of speech variability are being further enhanced by novel use and development of neurocomputational models of speech motor control. These models help to elucidate the mechanisms of motor control for speech through their encapsulation of speech motor theory and their correspondence with human neuroanatomy. I have been working to utilize neurocomputational models of speech motor control to provide motivation and support for specific predictions about the sources of speech production variability and the basis of coordinated speech behaviors.

Ramanarayanan, V., Lammert, A., Goldstein, L. & Narayanan, S. (2014). Do articulatory settings facilitate efficient postural motor control of vocal tract articulators? PLoS ONE 9(8), e104168.

Lammert, A., Shadle, C., Narayanan, S. & Quatieri, T. (2018). Speed-Accuracy Tradeoffs in Human Speech Production. PLoS ONE 13(9), e0202180.

Lammert, A., Parrell, B., Ciccarelli, G., Quatieri, T. (2019). Current Models of Speech Motor Control: A Control-Theoretic Overview of Architectures & Properties. The Journal of the Acoustical Society of America, 145(3), 1456-1481.

Parrell, B., & Lammert, A. (2019). Bridging Dynamical Systems and Optimal Trajectory Approaches to Speech Motor Control with Dynamic Movement Primitives. Frontiers in Psychology, 10, 2251.