Search Results for author: Abdelrahman Mohamed

Found 42 papers, 17 papers with code

Self-supervised Representation Learning for Speech Processing

1 code implementation NAACL (ACL) 2022 Hung-Yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff

Due to the growing popularity of SSL, and the shared mission of the areas in bringing speech and language technologies to more use cases with better quality and scaling the technologies for under-represented languages, we propose this tutorial to systematically survey the latest SSL techniques, tools, datasets, and performance achievement in speech processing.

Representation Learning

Self-Supervised Models of Speech Infer Universal Articulatory Kinematics

no code implementations16 Oct 2023 Cheol Jun Cho, Abdelrahman Mohamed, Alan W Black, Gopala K. Anumanchipalli

Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks.

Self-Supervised Learning

SD-HuBERT: Self-Distillation Induces Syllabic Organization in HuBERT

no code implementations16 Oct 2023 Cheol Jun Cho, Abdelrahman Mohamed, Shang-Wen Li, Alan W Black, Gopala K. Anumanchipalli

Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing.

Language Modelling Self-Supervised Learning

Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond

no code implementations9 Oct 2023 Jiatong Shi, William Chen, Dan Berrebbi, Hsiu-Hsuan Wang, Wei-Ping Huang, En-Pei Hu, Ho-Lam Chuang, Xuankai Chang, Yuxun Tang, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Shinji Watanabe

The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification.

Language Identification speech-recognition +1

Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model

1 code implementation19 May 2023 Puyuan Peng, Shang-Wen Li, Okko Räsänen, Abdelrahman Mohamed, David Harwath

In this paper, we show that representations capturing syllabic units emerge when training a self-supervised speech model with a visually-grounded training objective.

Language Modelling Masked Language Modeling +1

ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

no code implementations18 May 2023 Jiatong Shi, Dan Berrebbi, William Chen, Ho-Lam Chung, En-Pei Hu, Wei Ping Huang, Xuankai Chang, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, Shinji Watanabe

Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks.

Automatic Speech Recognition Language Identification +3

Efficient Speech Representation Learning with Low-Bit Quantization

no code implementations14 Dec 2022 Ching-Feng Yeh, Wei-Ning Hsu, Paden Tomasello, Abdelrahman Mohamed

With the development of hardware for machine learning, newer models often come at the cost of both increased sizes and computational complexity.

Model Compression Quantization +1

Massively Multilingual ASR on 70 Languages: Tokenization, Architecture, and Generalization Capabilities

no code implementations10 Nov 2022 Andros Tjandra, Nayan Singhal, David Zhang, Ozlem Kalinli, Abdelrahman Mohamed, Duc Le, Michael L. Seltzer

Later, we use our optimal tokenization strategy to train multiple embedding and output model to further improve our result.

Biased Self-supervised learning for ASR

no code implementations4 Nov 2022 Florian L. Kreyssig, Yangyang Shi, Jinxi Guo, Leda Sari, Abdelrahman Mohamed, Philip C. Woodland

Furthermore, this paper proposes a variant of MPPT that allows low-footprint streaming models to be trained effectively by computing the MPPT loss on masked and unmasked frames.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech

no code implementations21 Oct 2022 Cheol Jun Cho, Peter Wu, Abdelrahman Mohamed, Gopala K. Anumanchipalli

Recent self-supervised learning (SSL) models have proven to learn rich representations of speech, which can readily be utilized by diverse downstream tasks.

Self-Supervised Learning

D3Former: Debiased Dual Distilled Transformer for Incremental Learning

1 code implementation25 Jul 2022 Abdelrahman Mohamed, Rushali Grandhe, K J Joseph, Salman Khan, Fahad Khan

In contrast to a recent ViT based CIL approach, our $\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when new tasks are learned and remains suitable for a large number of incremental tasks.

Incremental Learning

STOP: A dataset for Spoken Task Oriented Semantic Parsing

1 code implementation29 Jun 2022 Paden Tomasello, Akshat Shrivastava, Daniel Lazar, Po-chun Hsu, Duc Le, Adithya Sagar, Ali Elkahky, Jade Copet, Wei-Ning Hsu, Yossi Adi, Robin Algayres, Tu Ahn Nguyen, Emmanuel Dupoux, Luke Zettlemoyer, Abdelrahman Mohamed

Furthermore, in addition to the human-recorded audio, we are releasing a TTS-generated version to benchmark the performance for low-resource domain adaptation of end-to-end SLU systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

LegoNN: Building Modular Encoder-Decoder Models

no code implementations7 Jun 2022 Siddharth Dalmia, Dmytro Okhonko, Mike Lewis, Sergey Edunov, Shinji Watanabe, Florian Metze, Luke Zettlemoyer, Abdelrahman Mohamed

We describe LegoNN, a procedure for building encoder-decoder architectures in a way so that its parts can be applied to other tasks without the need for any fine-tuning.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Self-Supervised Speech Representation Learning: A Review

no code implementations21 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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT

1 code implementation15 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.

Representation Learning Speaker Verification

Federated Learning with Partial Model Personalization

2 code implementations8 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.

Federated Learning

textless-lib: a Library for Textless Spoken Language Processing

1 code implementation NAACL (ACL) 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.


Object Detection in Aerial Images: What Improves the Accuracy?

no code implementations21 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.

object-detection Object Detection In Aerial Images

Robust Self-Supervised Audio-Visual Speech Recognition

1 code implementation5 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 #2 on Audio-Visual Speech Recognition on LRS3-TED (using extra training data)

Audio-Visual Speech Recognition Automatic Speech Recognition +5

Textless Speech Emotion Conversion using Discrete and Decomposed Representations

no code implementations14 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.

Scaling ASR Improves Zero and Few Shot Learning

no code implementations10 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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Text-Free Prosody-Aware Generative Spoken Language Modeling

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.

Language Modelling

HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

8 code implementations14 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)

Clustering Language Modelling +2

The Obata first eigenvalue theorems on a seven dimensional quaternionic contact manifold

no code implementations31 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

Transformers with convolutional context for ASR

4 code implementations26 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.

Image Classification Machine Translation +4

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