N
Navdeep Jaitly
Researcher at Google
Publications - 132
Citations - 36853
Navdeep Jaitly is an academic researcher from Google. The author has contributed to research in topics: Recurrent neural network & Artificial neural network. The author has an hindex of 51, co-authored 124 publications receiving 30634 citations. Previous affiliations of Navdeep Jaitly include Environmental Molecular Sciences Laboratory & University of Toronto.
Papers
More filters
Journal ArticleDOI
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Journal Article
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
Proceedings ArticleDOI
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
TL;DR: Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers is presented.
Proceedings Article
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Alex Graves,Navdeep Jaitly +1 more
TL;DR: A speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation is presented, based on a combination of the deep bidirectional LSTM recurrent neural network architecture and the Connectionist Temporal Classification objective function.
Proceedings ArticleDOI
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Jonathan Shen,Ruoming Pang,Ron Weiss,Mike Schuster,Navdeep Jaitly,Zongheng Yang,Zhifeng Chen,Yu Zhang,Yuxuan Wang,Rj Skerrv-Ryan,Rif A. Saurous,Yannis Agiomvrgiannakis,Yonghui Wu +12 more
TL;DR: Tacotron 2, a neural network architecture for speech synthesis directly from text that is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those Spectrograms is described.