My research is focused on behavioral analytics for neurocognitive assessment, developing novel methods that enable machines to find meaningful patterns in human behavior, and gain awareness of human neurocognitive states. Human behavior represents the final pathway for a variety of functions in the brain, and acts as the primary interface individuals have to the systems and agents with which they interact. Complex behaviors that humans exhibit - especially sensorimotor behaviors, such as walking or speaking - are intricately choreographed in space and time, requiring the precise engagement of various neural processes. Algorithms with enhanced theoretical foundations in neural and cognitive science can shed light onto the fundamental principles and mechanisms underlying human behavior, thereby improving their potential impact in health technologies, as well as technologies for the advancement of basic science. At the same time, systems equipped with algorithms for understanding human states can better adapt the nature and extent of machine engagement in human-machine collaborations, potentially improving collaborative outcomes and human performance. Fresh insights into developing algorithms that can uncover the mechanisms and sources human behavior have been recently enabled by an interdisciplinary confluence of empirical, computational and theoretical advances. My current research is situated at this confluence.

Speech Production & Perception: My interests all come together very concretely in the domain of human speech production. Much of my current work is focused on perception-action mechanisms related to speech, which are some of the most crucial that humans posses. The ability to produce and perceive speech forms much of the basis for human communication, expression and social interaction.

Selected references:

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

Sloboda, J., Lammert, A., Williamson, J., Smalt, C., Mehta, D., Curry, I., Heaton, K., Palmer, J. & Quatieri, T. (2018). Vocal biomarkers for cognitive performance estimation in a working memory task. In INTERSPEECH-2018.

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., 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., Proctor, M. and Narayanan, S. (2013). Morphological Variation in the Adult Hard Palate and Posterior Pharyngeal Wall. Journal of Speech, Language and Hearing Research, 56: 521-530.

Divenyi, P., and 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.

Models of Sensorimotor Control: One of my longest-standing research interests is in robotic, computational and neural models of sensorimotor. Models serve to encapsulate current understanding of control mechanisms, and make specific predictions about the nature and causes of behavior.

Selected references:

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

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

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

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).

Lammert, A., Goldstein, L., Narayanan, S. and Iskarous, K. (2013). Statistical Methods for Estimation of Direct and Differential Kinematics of the Vocal Tract. Speech Communication, 55: 147-161.

Long, J.H. Jr., Koob, T.J., Irving, K., Combie, K., Engel, V., Livingston, N., Lammert, A.C. and Schumacher, J. (2006). Biomimetic evolutionary analysis: Testing the adaptive value of vertebrate tail stiffness in autonomous swimming robots. J. Experimental Biology, 209: 4732-4746.

Long, J.H. Jr., Lammert, A.C., Pell, C.A., Kemp, M., Strother, J., Crenshaw, H.C. and McHenry, M.J. (2004). A navigational primitive: biorobotic implementation of cycloptic helical klinotaxis in planar motion. IEEE J. Oceanic Engineering, 29: 795-806.

Speech Technologies: Problems of interest to me often intersect with technology in one of two ways. First, they require the collection, processing and modeling of novel data in order to drive the research forward. Second, proposed solutions to these problems often have direct technological applications. I attempt to explore both of these connections in my work.

Selected references:

Hagedorn, C., Sorensen, T., Lammert, A.C., Toutios, A., Goldstein, L.M., Byrd, D., & Narayanan, S.S. (in revision). Engineering Innovation in Speech Science: Data and Technologies. Submitted to Perspectives of the American Speech and Hearing Association Special Interest Groups in May, 2018.

Sorensen, T., Skordilis, Z., Toutios, A., Kim, Y.-C., Zhu, Y., Kim, J., Lammert, A., Ramanarayanan, V., Goldstein, L., Byrd, D., Nayak, K. & Narayanan, S. (2017). Database of volumetric and real-time vocal tract MRI for speech science. In INTERSPEECH-2017.

Li, M., Kim, J., Lammert, A., Ghosh, P.K., Ramanarayanan, V. & Narayanan, S. (2016). Speaker verification based on the fusion of speech acoustics and inverted signals. Computer, Speech & Language, 36: 196-211.

Narayanan, S., Bresch, E., Ghosh, P., Goldstein, L., Katsamanis, A., Kim, Y., Lammert, A., Proctor, M., Ramanarayanan, V., & Zhu, Y. (2011). A Multimodal Real-Time MRI Articulatory Corpus for Speech Research. In INTERSPEECH-2011, 837-840.

Lammert, A., Proctor, M., and Narayanan, S. (2010). Data-Driven Analysis of Realtime Vocal Tract MRI using Correlated Image Regions. In Proceedings of Interspeech 2010 in Makuhari, Japan.

Ramanarayanan, V., Ghosh, P.K., Lammert, A. and Narayanan, S. (2012). Exploiting speech production information for automatic speech and speaker modeling and recognition - possibilities and new opportunities. In APSIPA-2012.

Lammert, A., Ellis, D. and Divenyi, P. (2008). Data-driven articulatory inversion incorporating articulator priors. In Proceedings of the ISCA Workshop on Statistical and Perceptual Audition. International Speech Communication Association, Brisbane, Australia.

Human Expression & Interaction: I'm also interested in understanding complex human behaviors, a notoriously difficult task. It is possible, however, to accurately model key aspects of the way people act and interact using low-level perceptual features from high-quality data, combined with carefully trained statistical models.

Selected references:

Black, M.P, Katsamanis, A., Baucom, B.R., Lee, C.-C., Lammert, A.C., Christensen, A., Georgiou, P.G. & Narayanan, S.S. (2013). Toward automating a human behavioral coding system for married couples' interactions using acoustic features. Speech Communication, 55: 1-21.

Georgiou, P.G., Black, M.P., Lammert, A.C., Baucom, B.R. & Narayanan, S.S. (2011). ``That's aggravating, very aggravating'': Is it possible to classify behaviors in couple interactions using automatically derived lexical features? In ACII-2011.

Lee, C., Black, M., Katsamanis, A., Lammert, A., Baucom, B., Christensen, A., Panayiotis, G., and Narayanan, S. (2010). Quantification of Prosodic Entrainment in Affective Spontaneous Spoken Interactions of Married Couples. In Proceedings of Interspeech 2010 in Makuhari, Japan.