5.7k citations · 8.2k across the 48 of their papers we have counts for
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Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation
Ye Jia, Melvin Johnson, Wolfgang Macherey +6
End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models…
Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron J. Weiss +9
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training d…
Training Deeper Neural Machine Translation Models with Transparent Attention
Ankur Bapna, Mia Xu Chen, Orhan Firat +2
While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models…