282 citations · 1.2k across the 80 of their papers we have counts for
5 papers · 1 filter
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
Parnia Bahar, Christopher Brix, Hermann Ney
This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLS…
On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation
Tamer Alkhouli, Gabriel Bretschner, Hermann Ney
This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in tran…
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition
Tobias Menne, Ralf Schlüter, Hermann Ney
This paper presents, in the context of multi-channel ASR, a method to adapt a mask based, statistically optimal beamforming approach to a speaker of interest. The beamforming vecto…
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
Albert Zeyer, Tamer Alkhouli, Hermann Ney
We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We sh…
Improved training of end-to-end attention models for speech recognition
Albert Zeyer, Kazuki Irie, Ralf Schlüter +1
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competit…