Cyril Allauzen
Cyril Allauzen is a research scientist at Google in New York. His main research interests are in finite-state methods and their applications to text, speech and natural language processing and machine learning. Before joining Google, he worked as a researcher at AT&T Labs Research and at NYU's Courant Institute of Mathematical Sciences. Cyril received his Ph.D. in computer science from the Université de Marne-la-Vallée in 2001.
Cyril is an author of the OpenFst Library, the OpenKernel Library and the GRM Library.
Authored Publications
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Google
On Weight Interpolation of the Hybrid Autoregressive Transducer Model
David Rybach
Interspeech 2022, Interspeech 2022 (2022) (to appear)
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR
Cal Peyser
Zhiyun Lu
David Rybach
Interspeech 2022 (2022) (to appear)
An Efficient Streaming Non-Recurrent On-Device End-to-End Model with Improvements to Rare-Word Modeling
Ruoming Pang
Cal Peyser
Yu Zhang
James Qin
Wei Li
Yonghui Wu
Anmol Gulati
Chung-Cheng Chiu
Jiahui Yu
David Johannes Rybach
Emmanuel Guzman
Rami Botros
Qiao Liang
Quoc-Nam Le-The
Interspeech (2021) (to appear)
Hybrid Autoregressive Transducer (HAT)
David Rybach
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, pp. 6139-6143
Federated Learning of N-gram Language Models
Adeline Wong
The SIGNLL Conference on Computational Natural Language Learning (2019)
Contextual Recovery of Out-of-Lattice Named Entities in Automatic Speech Recognition
Jack Serrino
ISCA Interspeech 2019, ISCA, Graz, Austria (2019), pp. 3830-3834
Algorithms for Weighted Finite Automata with Failure Transitions
International Conference of Implementation and Applications of Automata (CIAA) (2018), pp. 46-58
Transliterated mobile keyboard input via weighted finite-state transducers
Prasoon Goyal
David Rybach
Lars Hellsten
Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing (FSMNLP) (2017)
Distributed representation and estimation of WFST-based n-gram models
Proceedings of the ACL Workshop on Statistical NLP and Weighted Automata (StatFSM) (2016), pp. 32-41
