Kyle Gorman is a computational linguist who specializes in speech and language processing. His work focuses on applications such as multilingual speech recognition and synthesis, characterization and diagnosis of developmental and degenerative disorders, and the cognitive underpinnings of language.
Gorman’s contributions to emerging linguistic technologies and applications are wide-ranging. He is the principal author of Pynini
, a powerful weighted finite-state grammar extension for Python that is used for speech and language processing at Google. Gorman is currently preparing a monograph on Pynini, entitled Finite State Text Processing.
Along with a team of Graduate Center researchers, Gorman helped create a new open-source software that may speed the development of speech technologies such as recognizers and synthesizers (much like Siri) in new languages. The software, WikiPron, generates pronunciations based on data produced by volunteers that write and edit Wiktionary, an open-access platform. Gorman’s lab utilized WikiPron in a collaborative online project that encourages developers to produce artificial intelligence tools to predict the pronunciation of unfamiliar words in different languages.
Gorman came to The Graduate Center with substantial expertise in the high-tech sector. He spent several years as a software engineer at Google Research in New York City, where he collaborated on the OpenFst
libraries and developed algorithms used in Google speech products like Maps and Google Now. Prior to joining Google, Gorman was an assistant professor at the Center for Spoken Language Understanding at Oregon Health & Science University in Portland.
Awards and Grants
Professional Affiliations and Memberships
- Best Paper Award at the Sixth Workshop on Noisy User-Generated Text for "Detecting objectifying language in online professor reviews" (2020).
- Outstanding Paper Award at the 57th Annual Meeting of the Association for Computational Linguistics for “We need to talk about standard splits” (2019).
- Association for Computational Linguistics
- Institute of Electrical and Electronics Engineers
- Linguistics Society of America
- Methods in Computational Linguistics I
- Methods in Computational Linguistics II
- Statistics for Linguistic Research
- Seminar in Writing Systems
- Language Technology
- Kyle Gorman, Lucas F.E. Ashby, Aaron Goyzueta, Arya D. McCarthy, Shijie Wu, and Daniel You (2020). The SIGMORPHON 2020 shared task on multilingual grapheme-to-phoneme conversion. In 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, in press.
- Jackson L. Lee, Lucas F.E. Ashby, M. Elizabeth Garza, Yeonju Lee-Sikka, Sean Miller, Alan Wong, Arya D. McCarthy, and Kyle Gorman (2020). Massively multilingual pronunciation mining with WikiPron. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4223-4228.
- Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekaterina Vylomova, Sabrina J. Mielke, Garrett Nicolai, Miikka Silfverberg, Timofey Arkhangelskij, Natalya Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ernštreits, Yuval Pinter, Cassandra L. Jacobs, Ryan Cotterell, Mans Hulden, and David Yarowsky. (2020). UniMorph 3.0: universal morphology. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 3922-3931.
- Kyle Gorman, Arya D. McCarthy, Ryan Cotterell, Ekaterina Vylomova, Miikka Silfverberg, and Magdalena Markowska (2019). Weird inflects but OK: making sense of morphological generation errors. In Proceedings of the 23rd Conference on Computational Natural Language Learning, pages 140-151.
- Kyle Gorman and Charles Yang (2019). When nobody wins. In Franz Rainer, Francesco Gardani, Hans Christian Luschützky and Wolfgang U. Dressler (ed.), Competition in inflection and word formation, pages 169-193. Dordrecht: Springer.
- Sandy Ritchie, Richard Sproat, Kyle Gorman, Daan van Esch, Christian Schallhart, Nikos Bampounis, Benoît Brard, Jonas Fromseier Mortensen, Millie Holt, and Eoin Mahon (2019). Unified verbalization for speech recognition & synthesis across languages. In INTERSPEECH, pages 3530-3534.
- Kyle Gorman and Steven Bedrick (2019). We need to talk about standard splits. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2786-2791. (Outstanding paper award)
- Sabrina J. Mielke, Ryan Cotterell, Kyle Gorman, Brian Roark, and Jason Eisner (2019). What kind of language is hard to language-model? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4975-4989.
- Hao Zhang, Richard Sproat, Axel H. Ng, Felix Stahlberg, Xiaochang Peng, Kyle Gorman, and Brian Roark (2019). Neural models of text normalization for speech applications. Computational Linguistics 45(2): 293-337.
- Kyle Gorman, Gleb Mazovetskiy, and Vitaly Nikolaev (2018). Improving homograph disambiguation with machine learning. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation, pages 1349-1352.
- Axel H. Ng, Kyle Gorman, and Richard Sproat (2017). Minimally supervised written-to-spoken text normalization. In ASRU, pages 665-670.
- Joel Adams, Steven Bedrick, Gerasimos Fergadiotis, Kyle Gorman, and Jan van Santen (2017). Target word prediction and paraphasia classification in spoken discourse. In Proceedings of the BioNLP Workshop, pages 1-8.
- Heather MacFarlane, Kyle Gorman, Rosemary Ingham, Alison Presmanes Hill, Katina Papadakis, Géza Kiss, and Jan van Santen (2017). Quantitative analysis of disfluency in children with autism spectrum disorder or language impairment. PLOS ONE 12(3): e0173936.
- Gerasimos Fergadiotis, Kyle Gorman, and Steven Bedrick (2016). Algorithmic classification of five characteristic types of paraphasias. American Journal of Speech-Language Pathology 25(4S): S776-S787.
- Kyle Gorman and Richard Sproat (2016). Minimally supervised number normalization. Transactions of the Association for Computational Linguistics 4: 507-519.
- Kyle Gorman (2016). Pynini: A Python library for weighted finite-state grammar compilation. In Proceedings of the ACL Workshop on Statistical NLP and Weighted Automata, pages 75-80.
- Kyle Gorman, Lindsay Olson, Alison Presmanes Hill, Rebecca Lunsford, Peter Heeman, and Jan van Santen (2016). Uh and um in children with autism spectrum disorders or language impairment. Autism Research 9(8): 854-865.
- Alison Presmanes Hill, Jan van Santen, Kyle Gorman, Beth H. Langhorst, and Eric Fombonne (2015). Memory in language-impaired children with and without autism. Journal of Neurodevelopmental Disorders 7: 19.
- Kyle Gorman, Steven Bedrick, Géza Kiss, Eric Morley, Rosemary Ingham, Metrah Mohammad, Katina Papadakis, and Jan van Santen (2015). Automated morphological analysis of clinical language samples. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology Workshop, pages 108-116.
- Maider Lehr, Kyle Gorman, and Izhak Shafran (2014). Discriminative pronunciation modeling for dialectal speech recognition. In INTERSPEECH, pages 1458-1462.
- Lars Hinrichs, Axel Bohmann, and Kyle Gorman (2013). Real-time trends in the Texas English vowel system: F2 trajectory in GOOSE as an index of a variety's ongoing delocalization. Rice Working Papers in Linguistics 4: 1-12.
- Kyle Gorman (2013). Generative phonotactics. University of Pennsylvania dissertation.
- Kyle Gorman and Daniel Ezra Johnson (2013). Quantitative analysis. In Robert Bayley, Richard Cameron, and Ceil Lucas (ed.), The Oxford handbook of sociolinguistics, pages 214-240. Oxford: Oxford University Press.
- Kyle Gorman (2012). Exceptions to rhotacism. In Proceedings of the 48th annual meeting of the Chicago Linguistic Society, pages 279-293.
- Kyle Gorman and Richard Sproat. In press. Finite-State Text Processing. Morgan & Claypool.
- Angie Waller and Kyle Gorman. 2020. Detecting objectifying language in online professor reviews. In Proceedings of the Sixth Workshop on Noisy User-generated Text, 171-180.
- Piotr Szymański and Kyle Gorman. 2020. Is the best better? Bayesian statistical model comparison for natural language processing. In Empirical Methods in Natural Language Processing, pages 2203-2212.