CV

Education

PhD in Linguistics, Brown University (In progress, 2021–Present)
MA in Linguistics, UC Davis (2019–2021)
BA in Linguistics, University of California, Davis (2015–2019) — With Highest Honors
BA in English Literature, University of California, Davis (2015–2019) — With Highest Honors

Ferenc Segedin, B., & Cohen Priva, U. A large-scale investigation of vowel co-occurrence patterns in the world’s lexicons (Under Revision).
Cohn, M., Ferenc Segedin, B., & Zellou, G. (2022). Acoustic-phonetic properties of Siri- and human-directed speech. Journal of Phonetics, 90, 101123.
Zellou, G., Cohn, M., & Ferenc Segedin, B. (2021). Age- and gender-related differences in speech alignment toward humans and voice-AI. Frontiers in Communication, 5, 600361.
Zellou, G., Barreda, S., & Ferenc Segedin, B. (2020). Partial perceptual compensation for nasal coarticulation is robust to fundamental frequency variation. The Journal of the Acoustical Society of America, 147(3), EL271–EL276.

Ferenc Segedin, B. (2025). Sub-phonemic featural dimensions mediate consonantal co-occurrence biases in a cross-linguistically consistent manner. In Proceedings of the Annual Meeting of the Cognitive Science Society, 47.
Cohn, M., Ferenc Segedin, B., & Zellou, G. (2019). Imitating Siri: Socially-mediated vocal alignment to device and human voices. In Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 19).
Ferenc Segedin, B., Cohn, M., & Zellou, G. (2019). Perceptual adaptation to device and human voices: Learning and generalization of a phonetic shift across real and voice-AI talkers. In Proceedings of Interspeech 2019 (pp. 2310–2314).
Zellou, G., & Ferenc Segedin, B. (2019). The distribution of coarticulatory variation influences perceptual adaptation. In Proceedings of the 19th International Congress of Phonetic Sciences (ICPhS 19).

Ferenc Segedin, B. (2025). A technique for isolating lexically-independent phonetic dependencies in generative CNNs. arXiv preprint.
Ferenc Segedin, B., & Beguš, G. (2025). Exploring the encoding of linguistic representations in the Fully-Connected Layer of generative CNNs for speech. arXiv preprint arXiv:2501.07726.

Ferenc Segedin, B. (2026, January). Investigating the extent of positional biases in bowel co-occurrence dependencies across languages. Accepted for presentation at the 100th Annual Meeting of the Linguistic Society of America (LSA), New Orleans, LA.
Ferenc Segedin, B. (2025). Evaluating Wasserstein GAN discriminators as models of human well-formedness judgments. Talk presented at the AMP 2025 Special Session on Deep Phonology, University of California, Berkeley.
Ferenc Segedin, B. (2025). Sub-phonemic featural dimensions mediate consonantal co-occurrence biases in a cross-linguistically consistent manner. Talk presented at the 47th Annual Meeting of the Cognitive Science Society (CogSci 2025).

Ferenc Segedin, B. (2024). Cross-linguistic evidence for a segmental identity bias in vowel-vowel co-occurrence patterns. Poster presented at the 98th Annual Meeting of the Linguistic Society of America (LSA), New York City, USA.
Ferenc Segedin, B., Cohn, M., & Zellou, G. (2019). Perceptual adaptation to device and human voices: Learning and generalization of a phonetic shift across real and voice-AI talkers. Poster presented at Interspeech 2019, Graz, Austria.

Chen, J., Zhou, A., Ray Barman, S., Beguš, G., & Ferenc Segedin, B. (2025). Deep Language Learning: Modeling language from raw speech (Part 1). Tutorial presented at the 99th Annual Meeting of the Linguistic Society of America (LSA), New York City, NY, January 10.