258 citations · 450 across the 4 of their papers we have counts for
8 papers
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition
Shigeki Karita, Yotaro Kubo, Michiel Adriaan Unico Bacchiani +1
End-to-end (E2E) modeling is advantageous for automatic speech recognition (ASR) especially for Japanese since word-based tokenization of Japanese is not trivial, and E2E modeling…
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
Yuma Koizumi, Shigeki Karita, Scott Wisdom +4
Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an analysis/synthesis filterbank with a mask prediction network,…
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar, Wei Ding, Llion Jones +6
Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in soft…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Character-Level Language Modeling with Deeper Self-Attention
Rami Al-Rfou, Dokook Choe, Noah Constant +2
LSTMs and other RNN variants have shown strong performance on character-level language modeling. These models are typically trained using truncated backpropagation through time, an…
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Mia Xu Chen, Orhan Firat, Ankur Bapna +9
The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed…