2.2k citations · 3.7k across the 33 of their papers we have counts for
18 papers · 1 filter
Transfer Learning for Structured Pruning under Limited Task Data
Lucio Dery, David Grangier, Awni Hannun
Large, pre-trained models are problematic to use in resource constrained applications. Fortunately, task-aware structured pruning methods offer a solution. These approaches reduce…
Parallel Composition of Weighted Finite-State Transducers
Shubho Sengupta, Vineel Pratap, Awni Hannun
Finite-state transducers (FSTs) are frequently used in speech recognition. Transducer composition is an essential operation for combining different sources of information at differ…
The History of Speech Recognition to the Year 2030
Awni Hannun
The decade from 2010 to 2020 saw remarkable improvements in automatic speech recognition. Many people now use speech recognition on a daily basis, for example to perform voice sear…
Iterative Pseudo-Labeling for Speech Recognition
Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn +3
Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition (ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which efficiently…
Semi-Supervised Speech Recognition via Local Prior Matching
Wei-Ning Hsu, Ann Lee, Gabriel Synnaeve +1
For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by…
Scaling Up Online Speech Recognition Using ConvNets
Vineel Pratap, Qiantong Xu, Jacob Kahn +6
We design an online end-to-end speech recognition system based on Time-Depth Separable (TDS) convolutions and Connectionist Temporal Classification (CTC). We improve the core TDS a…
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition
Adrien Dufraux, Emmanuel Vincent, Awni Hannun +2
The transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many erro…
Self-Training for End-to-End Speech Recognition
Jacob Kahn, Ann Lee, Awni Hannun
We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model…
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…
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions
Awni Hannun, Ann Lee, Qiantong Xu +1
We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnit…
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…
wav2letter++: The Fastest Open-source Speech Recognition System
Vineel Pratap, Awni Hannun, Qiantong Xu +5
This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor libra…
An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
Chris Lengerich, Awni Hannun
We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurr…
Learning Multiscale Features Directly From Waveforms
Zhenyao Zhu, Jesse H. Engel, Awni Hannun
Deep learning has dramatically improved the performance of speech recognition systems through learning hierarchies of features optimized for the task at hand. However, true end-to-…
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Dario Amodei, Rishita Anubhai, Eric Battenberg +31
We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pip…
Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper +8
We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which r…
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
Awni Y. Hannun, Andrew L. Maas, Daniel Jurafsky +1
We present a method to perform first-pass large vocabulary continuous speech recognition using only a neural network and language model. Deep neural network acoustic models are now…
Building DNN Acoustic Models for Large Vocabulary Speech Recognition
Andrew L. Maas, Peng Qi, Ziang Xie +4
Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recognition systems. Building neural network acoustic models requires several design d…