Event Details

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While the “deep learning tsunami” defines the state of the art in speech and language processing, finite-state transducer grammars developed by linguists and engineers are still widely used in highly-multilingual settings, particularly for “front-end” speech applications. In this talk, I will first briefly review the current state of the OpenFst and OpenGrm finite-state transducer libraries. I will then discuss several recent innovations in the finite-state world. These include algorithms for inducing text normalization and grapheme-to-phoneme grammars from parallel data, heuristic optimization of arbitrary weighted transducers, and an algorithm for efficiently computing the single shortest string of a wider variety of non-deterministic weighted acceptors.

Speaker Bio

Kyle Gorman is an assistant professor of linguistics at the Graduate Center, City University of New York, and director of the master’s program in computational linguistics; he is also a software engineer in the speech and language algorithms group at Google. With Richard Sproat, he is the coauthor of Finite-State Text Processing (Morgan & Claypool, 2021) and the creator of Pynini, a finite-state text processing library for Python. He has also published on statistical methods for comparing computational models, text normalization, grapheme-to-phoneme conversion, and morphological analysis, as well as many topics in linguistic theory.

The recording for this AI Seminar talk will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.