b'Michael Auli'

research

∙ 05/22/2023

Scaling Speech Technology to 1,000+ Languages

Expanding the language coverage of speech technology has the potential t...

0 Vineel Pratap, et al. ∙

research

∙ 05/17/2023

DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning

In this paper, we introduce self-distillation and online clustering for ...

0 Alexander H. Liu, et al. ∙

research

∙ 02/10/2023

AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations

Self-supervision has shown great potential for audio-visual speech recog...

0 Jiachen Lian, et al. ∙

research

∙ 12/14/2022

Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language

Current self-supervised learning algorithms are often modality-specific ...

0 Alexei Baevski, et al. ∙

research

∙ 10/18/2022

Simple and Effective Unsupervised Speech Translation

The amount of labeled data to train models for speech tasks is limited f...

0 Changhan Wang, et al. ∙

research

∙ 07/13/2022

Masked Autoencoders that Listen

This paper studies a simple extension of image-based Masked Autoencoders...

3 Po-Yao Huang, et al. ∙

research

∙ 06/27/2022

Wav2Vec-Aug: Improved self-supervised training with limited data

Self-supervised learning (SSL) of speech representations has received mu...

0 Anuroop Sriram, et al. ∙

research

∙ 04/25/2022

On-demand compute reduction with stochastic wav2vec 2.0

Squeeze and Efficient Wav2vec (SEW) is a recently proposed architecture ...

0 Apoorv Vyas, et al. ∙

research

∙ 04/11/2022

Unified Speech-Text Pre-training for Speech Translation and Recognition

We describe a method to jointly pre-train speech and text in an encoder-...

1 Yun Tang, et al. ∙

research

∙ 04/06/2022

Simple and Effective Unsupervised Speech Synthesis

We introduce the first unsupervised speech synthesis system based on a s...

0 Alexander H. Liu, et al. ∙

research

∙ 04/05/2022

Towards End-to-end Unsupervised Speech Recognition

Unsupervised speech recognition has shown great potential to make Automa...

0 Alexander H. Liu, et al. ∙

research

∙ 03/21/2022

XTREME-S: Evaluating Cross-lingual Speech Representations

We introduce XTREME-S, a new benchmark to evaluate universal cross-lingu...

0 Alexis Conneau, et al. ∙

research

∙ 03/01/2022

Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training

Human speech data comprises a rich set of domain factors such as accent,...

0 Ramon Sanabria, et al. ∙

research

∙ 02/07/2022

data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

While the general idea of self-supervised learning is identical across m...

0 Alexei Baevski, et al. ∙

research

∙ 11/17/2021

XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

This paper presents XLS-R, a large-scale model for cross-lingual speech ...

0 Arun Babu, et al. ∙

research

∙ 09/23/2021

Simple and Effective Zero-shot Cross-lingual Phoneme Recognition

Recent progress in self-training, self-supervised pretraining and unsupe...

0 Qiantong Xu, et al. ∙

research

∙ 07/08/2021

Improved Language Identification Through Cross-Lingual Self-Supervised Learning

Language identification greatly impacts the success of downstream tasks ...

0 Andros Tjandra, et al. ∙

research

∙ 05/24/2021

Unsupervised Speech Recognition

Despite rapid progress in the recent past, current speech recognition sy...

0 Alexei Baevski, et al. ∙

research

∙ 04/14/2021

Large-Scale Self- and Semi-Supervised Learning for Speech Translation

In this paper, we improve speech translation (ST) through effectively le...

0 Changhan Wang, et al. ∙

research

∙ 04/02/2021

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

Self-supervised learning of speech representations has been a very activ...

0 Wei-Ning Hsu, et al. ∙

research

∙ 01/26/2021

A Comparison of Approaches to Document-level Machine Translation

Document-level machine translation conditions on surrounding sentences t...

0 Zhiyi Ma, et al. ∙

research

∙ 12/30/2020

Reservoir Transformer

We demonstrate that transformers obtain impressive performance even when...

23 Sheng Shen, et al. ∙

research

∙ 11/13/2020

Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

Pre-training models on vast quantities of unlabeled data has emerged as ...

0 Shruti Bhosale, et al. ∙

research

∙ 10/24/2020

A Comparison of Discrete Latent Variable Models for Speech Representation Learning

Neural latent variable models enable the discovery of interesting struct...

0 Henry Zhou, et al. ∙

research

∙ 10/22/2020

Self-training and Pre-training are Complementary for Speech Recognition

Self-training and unsupervised pre-training have emerged as effective ap...

0 Qiantong Xu, et al. ∙

research

∙ 10/21/2020

Beyond English-Centric Multilingual Machine Translation

Existing work in translation demonstrated the potential of massively mul...

11 Angela Fan, et al. ∙

research

∙ 10/05/2020

Self-training Improves Pre-training for Natural Language Understanding

Unsupervised pre-training has led to much recent progress in natural lan...

0 Jingfei Du, et al. ∙

research

∙ 06/24/2020

Unsupervised Cross-lingual Representation Learning for Speech Recognition

This paper presents XLSR which learns cross-lingual speech representatio...

0 Alexis Conneau, et al. ∙

research

∙ 06/20/2020

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

We show for the first time that learning powerful representations from s...

0 Alexei Baevski, et al. ∙

research

∙ 03/24/2020

Robust and On-the-fly Dataset Denoising for Image Classification

Memorization in over-parameterized neural networks could severely hurt g...

0 Jiaming Song, et al. ∙

research

∙ 11/21/2019

Improving Conditioning in Context-Aware Sequence to Sequence Models

Neural sequence to sequence models are well established for applications...

0 Xinyi Wang, et al. ∙

research

∙ 11/10/2019

Effectiveness of self-supervised pre-training for speech recognition

We present pre-training approaches for self-supervised representation le...

0 Alexei Baevski, et al. ∙

research

∙ 10/22/2019

Depth-Adaptive Transformer

State of the art sequence-to-sequence models perform a fixed number of c...

0 Maha Elbayad, et al. ∙

research

∙ 10/12/2019

vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

We propose vq-wav2vec to learn discrete representations of audio segment...

0 Alexei Baevski, et al. ∙

research

∙ 09/28/2019

The Source-Target Domain Mismatch Problem in Machine Translation

While we live in an increasingly interconnected world, different places ...

0 Jiajun Shen, et al. ∙

research

∙ 08/15/2019

Simple and Effective Noisy Channel Modeling for Neural Machine Translation

Previous work on neural noisy channel modeling relied on latent variable...

0 Kyra Yee, et al. ∙

research

∙ 08/14/2019

On The Evaluation of Machine Translation Systems Trained With Back-Translation

Back-translation is a widely used data augmentation technique which leve...

0 Sergey Edunov, et al. ∙

research

∙ 07/22/2019

ELI5: Long Form Question Answering

We introduce the first large-scale corpus for long-form question answeri...

0 Angela Fan, et al. ∙

research

∙ 07/15/2019

Facebook FAIR's WMT19 News Translation Task Submission

This paper describes Facebook FAIR's submission to the WMT19 shared news...

0 Nathan Ng, et al. ∙

research

∙ 07/15/2019

GLOSS: Generative Latent Optimization of Sentence Representations

We propose a method to learn unsupervised sentence representations in a ...

0 Sidak Pal Singh, et al. ∙

research

∙ 04/11/2019

wav2vec: Unsupervised Pre-training for Speech Recognition

We explore unsupervised pre-training for speech recognition by learning ...

0 Steffen Schneider, et al. ∙

research

∙ 04/01/2019

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

fairseq is an open-source sequence modeling toolkit that allows research...

0 Myle Ott, et al. ∙

research

∙ 03/22/2019

Pre-trained Language Model Representations for Language Generation

Pre-trained language model representations have been successful in a wid...

0 Sergey Edunov, et al. ∙

research

∙ 03/19/2019

Cloze-driven Pretraining of Self-attention Networks

We present a new approach for pretraining a bi-directional transformer m...

0 Alexei Baevski, et al. ∙

research

∙ 02/20/2019

Mixture Models for Diverse Machine Translation: Tricks of the Trade

Mixture models trained via EM are among the simplest, most widely used a...

0 Tianxiao Shen, et al. ∙

research

∙ 01/29/2019

Pay Less Attention with Lightweight and Dynamic Convolutions

Self-attention is a useful mechanism to build generative models for lang...

0 Felix Wu, et al. ∙

research

∙ 01/21/2019

Modeling Human Motion with Quaternion-based Neural Networks

Previous work on predicting or generating 3D human pose sequences regres...

0 Dario Pavllo, et al. ∙

research

∙ 11/28/2018

3D human pose estimation in video with temporal convolutions and semi-supervised training

In this work, we demonstrate that 3D poses in video can be effectively e...

0 Dario Pavllo, et al. ∙

research

∙ 11/03/2018

Wizard of Wikipedia: Knowledge-Powered Conversational agents

In open-domain dialogue intelligent agents should exhibit the use of kno...

0 Emily Dinan, et al. ∙

research

∙ 09/28/2018

Adaptive Input Representations for Neural Language Modeling

We introduce adaptive input representations for neural language modeling...

0 Alexei Baevski, et al. ∙

Michael Auli

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