no code implementations • 21 May 2022 • Abdelrahman Mohamed, Hung-Yi Lee, Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaløe, Tara N. Sainath, Shinji Watanabe
Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.
no code implementations • 15 May 2022 • Bowen Shi, Abdelrahman Mohamed, Wei-Ning Hsu
This paper investigates self-supervised pre-training for audio-visual speaker representation learning where a visual stream showing the speaker's mouth area is used alongside speech as inputs.
no code implementations • ACL 2022 • Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino
Two pre-training configurations for speech translation and recognition, respectively, are presented to alleviate subtask interference.
no code implementations • 8 Apr 2022 • Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael Rabbat, Maziar Sanjabi, Lin Xiao
We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices.
no code implementations • 30 Mar 2022 • Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoit Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues.
1 code implementation • ACL 2022 • Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy T. Liu, Cheng-I Jeff Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee
In this paper, we introduce SUPERB-SG, a new benchmark focused on evaluating the semantic and generative capabilities of pre-trained models by increasing task diversity and difficulty over SUPERB.
1 code implementation • 9 Mar 2022 • Guan-Ting Lin, Yung-Sung Chuang, Ho-Lam Chung, Shu-wen Yang, Hsuan-Jui Chen, Shuyan Dong, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Lin-shan Lee
We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
1 code implementation • 15 Feb 2022 • Eugene Kharitonov, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Paden Tomasello, Ann Lee, Ali Elkahky, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources.
no code implementations • 21 Jan 2022 • Hashmat Shadab Malik, Ikboljon Sobirov, Abdelrahman Mohamed
In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images.
1 code implementation • ICLR 2022 • Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, Abdelrahman Mohamed
The lip-reading WER is further reduced to 26. 9% when using all 433 hours of labeled data from LRS3 and combined with self-training.
Ranked #1 on
Lipreading
on LRS3-TED
(using extra training data)
1 code implementation • 5 Jan 2022 • Bowen Shi, Wei-Ning Hsu, Abdelrahman Mohamed
Audio-based automatic speech recognition (ASR) degrades significantly in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe.
Ranked #1 on
Audio-Visual Speech Recognition
on LRS3-TED
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
no code implementations • arXiv 2021 • Felix Kreuk, Adam Polyak, Jade Copet, Eugene Kharitonov, Tu-Anh Nguyen, Morgane Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion.
no code implementations • 14 Nov 2021 • Felix Kreuk, Adam Polyak, Jade Copet, Eugene Kharitonov, Tu-Anh Nguyen, Morgane Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion.
no code implementations • 10 Nov 2021 • Alex Xiao, Weiyi Zheng, Gil Keren, Duc Le, Frank Zhang, Christian Fuegen, Ozlem Kalinli, Yatharth Saraf, Abdelrahman Mohamed
With 4. 5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition.
1 code implementation • ACL 2022 • Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu-Anh Nguyen, Morgane Rivière, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
Generative Spoken Language Modeling (GSLM) \cite{Lakhotia2021} is the only prior work addressing the generative aspects of speech pre-training, which replaces text with discovered phone-like units for language modeling and shows the ability to generate meaningful novel sentences.
no code implementations • 14 Jun 2021 • Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed
In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR).
4 code implementations • 14 Jun 2021 • Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation.
Ranked #3 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
3 code implementations • 3 May 2021 • Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Huang, Shuyan Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-Yi Lee
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
2 code implementations • 1 Apr 2021 • Adam Polyak, Yossi Adi, Jade Copet, Eugene Kharitonov, Kushal Lakhotia, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux
We propose using self-supervised discrete representations for the task of speech resynthesis.
no code implementations • 9 Mar 2021 • Alex Xiao, Christian Fuegen, Abdelrahman Mohamed
Pseudo-labeling is the most adopted method for pre-training automatic speech recognition (ASR) models.
no code implementations • 31 Dec 2020 • Abdelrahman Mohamed, Dimiter Vassilev
We show that a compact quaternionic contact manifold of dimension seven that satisfies a Lichnerowicz-type lower Ricci-type bound and has the $P$-function of any eigenfunction of the sub-Laplacian non-negative achieves its smallest possible eigenvalue only if the structure is qc-Einstein.
Differential Geometry Analysis of PDEs
no code implementations • 27 Oct 2019 • Kritika Singh, Dmytro Okhonko, Jun Liu, Yongqiang Wang, Frank Zhang, Ross Girshick, Sergey Edunov, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data.
3 code implementations • 26 Apr 2019 • Abdelrahman Mohamed, Dmytro Okhonko, Luke Zettlemoyer
The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition.