2.2k citations · 3.2k across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Principled Hybrids of Generative and Discriminative Domain Adaptation
Han Zhao, Zhenyao Zhu, Junjie Hu +2
We propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discrim…
Deep Voice: Real-time Neural Text-to-Speech
Sercan O. Arik, Mike Chrzanowski, Adam Coates +9
We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech…
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Sercan O. Arik, Markus Kliegl, Rewon Child +5
Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprin…