Text to Speech Papers
- Efficient Estimation of Word Representations in Vector Space
- Generating Sequences With Recurrent Neural Networks
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
- Neural Machine Translation by Jointly Learning to Align and Translate
- Sequence to Sequence Learning with Neural Networks
- End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
- Grammar as a Foreign Language
- On Using Monolingual Corpora in Neural Machine Translation
- Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
- Long Short-Term Memory-Networks for Machine Reading
- Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
- Recurrent Batch Normalization
- Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
- Layer Normalization
- Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
- Fully Character-Level Neural Machine Translation without Explicit Segmentation
- RNN Approaches to Text Normalization: A Challenge
- Tacotron: Towards End-to-End Speech Synthesis
- Simple Recurrent Units for Highly Parallelizable Recurrence
- DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
- Self-Attentive Residual Decoder for Neural Machine Translation
- AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
- Attention-based Wav2Text with Feature Transfer Learning
- Improving speech recognition by revising gated recurrent units
- Distributed Representations ofWords and Phrases and their Compositionality
- Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- GloVe: Global Vectors forWord Representation