Abigail Benecke

M.S. Student
Department of Psychological and Brain Sciences
Villanova University


Abigail Benecke is a first-year graduate student in the Psychology M.S. program at Villanova University. Her research focuses on speech perception and word recognition. She is currently working on her thesis with Dr. Joe Toscano in the Word Recognition and Auditory Perception (WRAP) Lab.

Contact Information

334 Tolentine Hall
Dept of Psychological & Brain Sciences
Villanova University
800 E Lancaster Ave
Villanova, PA 19085

Phone: (610) 310-5119
Fax: (610) 519-4269
Email: abenecke@villanova.edu


M.S. Psychology (in progress), Villanova University
B.A. Psychology and Music (2016), Magna cum laude, Susquehanna University


Broadly, Abigail is interested in elucidating how people are able to categorize and understand speech sounds in order to effectively communicate. Research questions are investigated using computational, phonetic, and behavioral approaches.

Computational models of speech perception

What information is necessary in order for human listeners to correctly categorize speech sounds? Are these cues combined and weighted according to reliability? Abigail is interested in investigating these questions by modeling the cognitive processes involved in speech perception. This section of research involves corpus analysis, programing computational models in R, and comparing multiple models of perception. Results from this project will be presented at Psychonomics in November 2017. Additionally, questions about what information is necessary to distinguish the voicing feature of word initial stop consonants are addressed in a paper to be submitted shortly.

Acoustic charactersitics of stop consonants

Though stop consonants are one of the most studied class of phonemes in the English language, there has been no comprehensive review of possible acoustic cues to stop consonant identity as there has been for vowels and fricatives. In order to provide a complete dataset for further phonetic research, Abigail is currently analyzing a large corpus of word initial stop consonants and summarizing the relevant characteristics. Results from this research will be submitted to the at the end of the summer 2017.

Cues at the level of individual tokens

For her thesis, Abigail will compare the collected phonetic data and computational results to human listener performance on individual speech tokens. In this way, it will be possible to analyze various models' ability to predict which phonemes listeners will accurately categorize versus more ambiguous exemplars. Additionally, individual tokens can be evaluated in terms of which cues are robust; that is, do listeners rely mostly on one or two primary and highly reliable cues by listeners or can multiple less robust cues be weighted during listener categorization to reach an accurate result? This second possiblity relies on two assumptions: (1) there are multiple cues present in each individual token and (2) human listeners actively rely on this information during categorization. Answers to these inquires will be pursued through behavioral experiments during the 2017-2018 academic year.