Zobrazeno 1 - 10 of 1 228 pro vyhledávání: '"A. Bruguier"'
1
Report
Partial Rewriting for Multi-Stage ASR
Autor: Bruguier, Antoine, Qiu, David, He, Yanzhang
For many streaming automatic speech recognition tasks, it is important to provide timely intermediate streaming results, while refining a high quality final result. This can be done using a multi-stage architecture, where a small left-context only mo
Externí odkaz: http://arxiv.org/abs/2312.09463
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2
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Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Autor: Hernandez, Steven M., Zhao, Ding, Ding, Shaojin, Bruguier, Antoine, Prabhavalkar, Rohit, Sainath, Tara N., He, Yanzhang, McGraw, Ian
Continued improvements in machine learning techniques offer exciting new opportunities through the use of larger models and larger training datasets. However, there is a growing need to offer these new capabilities on-board low-powered devices such a
Externí odkaz: http://arxiv.org/abs/2303.08343
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3
Akademický článek
Rare Mullerian adenosarcoma of the uterine cervix arising on a background of endometriosis: A diagnostic challenge with risk of malignant transformation—A case report and review of the current literature
Autor: Hannah Bruguier, Natalie Maalouf, Sarah Louise Smyth, Stephen Damato, Priyanka Reddy, Hooman Soleymani majd
Publikováno v: Clinical Case Reports, Vol 12, Iss 6, Pp n/a-n/a (2024)
Key Clinical Message Endometriosis may contribute to Mullerian adenosarcoma development but makes diagnosis challenging given similar symptoms. Survival benefit has not been definitively shown for chemotherapy, hormonal therapy, or radiotherapy, cons
Externí odkaz: https://doaj.org/article/a6e5aab7e6f84570a1a084e8ffa936bf
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6
Report
Neural-FST Class Language Model for End-to-End Speech Recognition
Autor: Bruguier, Antoine, Le, Duc, Prabhavalkar, Rohit, Li, Dangna, Liu, Zhe, Wang, Bo, Chang, Eun, Peng, Fuchun, Kalinli, Ozlem, Seltzer, Michael L.
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework. Our method utilize
Externí odkaz: http://arxiv.org/abs/2201.11867
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