72 citations · 131 across the 3 of their papers we have counts for
4 papers
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…
Active Learning for Speech Recognition: the Power of Gradients
Jiaji Huang, Rewon Child, Vinay Rao +3
In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples…