Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Paden Tomasello"'
Autor:
Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 11, Pp 250-266 (2023)
AbstractWe introduce dGSLM, the first “textless” model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with a dual-tower transformer architecture with cross-attent
Externí odkaz:
https://doaj.org/article/66cdeee2fe844d3386d07e74704b7b6a
Autor:
Ali Elkahky, Wei-Ning Hsu, Paden Tomasello, Tu-Anh Nguyen, Robin Algayres, Yossi Adi, Jade Copet, Emmanuel Dupoux, Abdelrahman Mohamed
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ffb3116b4ab2e7a213fe8daccf7b706
https://doi.org/10.1109/icassp49357.2023.10096788
https://doi.org/10.1109/icassp49357.2023.10096788
Autor:
Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
Publikováno v:
SLT-2022-IEEE Spoken Language Technology Workshop
SLT-2022-IEEE Spoken Language Technology Workshop, Jan 2023, Doha-Qatar, Qatar
SLT-2022-IEEE Spoken Language Technology Workshop, Jan 2023, Doha-Qatar, Qatar
We introduce dGSLM, the first “textless” model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with a dual-tower transformer architecture with cross-attention trai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d37c1eb6925f6c28882f0afb26690e23
Autor:
Michael Auli, Alexis Conneau, Tatiana Likhomanenko, Qiantong Xu, Paden Tomasello, Gabriel Synnaeve, Alexei Baevski, Ronan Collobert
Publikováno v:
ICASSP
Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they learn similar patterns or if they can be effectively combined. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2cbaeb347dc71d642bd291b9b8e4712c
https://doi.org/10.1109/icassp39728.2021.9414641
https://doi.org/10.1109/icassp39728.2021.9414641
Autor:
Ronan Collobert, Gabriel Synnaeve, Paden Tomasello, Vitaliy Liptchinsky, Awni Hannun, Vineel Pratap, Anuroop Sriram
Publikováno v:
INTERSPEECH
We study training a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages, and over-all simplifying deployment of ASR systems that support diverse languages. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef82d1005bea91eee550ee2ee3cd9637
http://arxiv.org/abs/2007.03001
http://arxiv.org/abs/2007.03001
Autor:
Ronan Collobert, Tatiana Likhomanenko, Vineel Pratap, Jacob Kahn, Qiantong Xu, Gabriel Synnaeve, Paden Tomasello, Gilad Avidov
Is pushing numbers on a single benchmark valuable in automatic speech recognition? Research results in acoustic modeling are typically evaluated based on performance on a single dataset. While the research community has coalesced around various bench
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cf9553b60db0ca07885d4718409a29a
Autor:
Romi Phadte, Sammy Sidhu, Gayatri Joshi, Matthew W. Moskewicz, Paras Jain, Forrest Iandola, Anting Shen, Nobie Redmon, Paden Tomasello
Publikováno v:
CVPR Workshops
Convolutional neural networks (CNNs) have become increasingly popular for solving a variety of computer vision tasks, ranging from image classification to image segmentation. Recently, autonomous vehicles have created a demand for depth information,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24ef972ce98755b6976d6b194dee89b5
https://doi.org/10.1109/cvprw.2019.00171
https://doi.org/10.1109/cvprw.2019.00171