2.2k citations · 2.5k across the 19 of their papers we have counts for
5 papers · 2 filters
Robust Speech Recognition Using Generative Adversarial Networks
Anuroop Sriram, Heewoo Jun, Yashesh Gaur +1
This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained wi…
Cold Fusion: Training Seq2Seq Models Together with Language Models
Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh +1
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and sp…
Exploring Neural Transducers for End-to-End Speech Recognition
Eric Battenberg, Jitong Chen, Rewon Child +8
In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any lang…
Reducing Bias in Production Speech Models
Eric Battenberg, Rewon Child, Adam Coates +13
Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and…
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling
Hairong Liu, Zhenyao Zhu, Xiangang Li +1
Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These methods suffer from two major drawbacks: 1) the set…