1.1k citations · 4.4k across the 113 of their papers we have counts for
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Libri-Light: A Benchmark for ASR with Limited or No Supervision
Jacob Kahn, Morgane Rivière, Weiyi Zheng +12
We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books f…
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures
Gabriel Synnaeve, Qiantong Xu, Jacob Kahn +6
We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functio…
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
Nicolas Carion, Gabriel Synnaeve, Alessandro Lazaric +1
Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they…
Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks
Andros Tjandra, Chunxi Liu, Frank Zhang +5
Deep acoustic models typically receive features in the first layer of the network, and process increasingly abstract representations in the subsequent layers. Here, we propose to f…
Why Build an Assistant in Minecraft?
Arthur Szlam, Jonathan Gray, Kavya Srinet +11
In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural la…
Growing Action Spaces
Gregory Farquhar, Laura Gustafson, Zeming Lin +3
In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress. In this work, we use a cu…
Word-level Speech Recognition with a Letter to Word Encoder
Ronan Collobert, Awni Hannun, Gabriel Synnaeve
We propose a direct-to-word sequence model which uses a word network to learn word embeddings from letters. The word network can be integrated seamlessly with arbitrary sequence mo…
Who Needs Words? Lexicon-Free Speech Recognition
Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert
Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words. In this paper, we show that character-based language models (LM) can perform as w…
A Fully Differentiable Beam Search Decoder
Ronan Collobert, Awni Hannun, Gabriel Synnaeve
We introduce a new beam search decoder that is fully differentiable, making it possible to optimize at training time through the inference procedure. Our decoder allows us to combi…