2.2k citations · 3.1k across the 11 of their papers we have counts for
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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…
Deep Speaker: an End-to-End Neural Speaker Embedding System
Chao Li, Xiaokong Ma, Bing Jiang +6
We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated…
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…
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…