282 citations · 1.2k across the 80 of their papers we have counts for
15 papers · 1 filter
Tight Integrated End-to-End Training for Cascaded Speech Translation
Parnia Bahar, Tobias Bieschke, Ralf Schlüter +1
A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation b…
Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
Parnia Bahar, Christopher Brix, Hermann Ney
Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not e…
Diving Deep into Context-Aware Neural Machine Translation
Jingjing Huo, Christian Herold, Yingbo Gao +3
Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation…
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition
Wei Zhou, Simon Berger, Ralf Schlüter +1
To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeli…
Early Stage LM Integration Using Local and Global Log-Linear Combination
Wilfried Michel, Ralf Schlüter, Hermann Ney
Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) for the task…
Fine-Grained Visual Classification with Efficient End-to-end Localization
Harald Hanselmann, Hermann Ney
The term fine-grained visual classification (FGVC) refers to classification tasks where the classes are very similar and the classification model needs to be able to find subtle di…
Investigation of Large-Margin Softmax in Neural Language Modeling
Jingjing Huo, Yingbo Gao, Weiyue Wang +2
To encourage intra-class compactness and inter-class separability among trainable feature vectors, large-margin softmax methods are developed and widely applied in the face recogni…
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models
Mohammad Zeineldeen, Albert Zeyer, Wei Zhou +3
Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units…
A New Training Pipeline for an Improved Neural Transducer
Albert Zeyer, André Merboldt, Ralf Schlüter +1
The RNN transducer is a promising end-to-end model candidate. We compare the original training criterion with the full marginalization over all alignments, to the commonly used max…
Robust Beam Search for Encoder-Decoder Attention Based Speech Recognition without Length Bias
Wei Zhou, Ralf Schlüter, Hermann Ney
As one popular modeling approach for end-to-end speech recognition, attention-based encoder-decoder models are known to suffer the length bias and corresponding beam problem. Diffe…
Context-Dependent Acoustic Modeling without Explicit Phone Clustering
Tina Raissi, Eugen Beck, Ralf Schlüter +1
Phoneme-based acoustic modeling of large vocabulary automatic speech recognition takes advantage of phoneme context. The large number of context-dependent (CD) phonemes and their h…
Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture
Christopher Brix, Parnia Bahar, Hermann Ney
Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computa…
When and Why is Unsupervised Neural Machine Translation Useless?
Yunsu Kim, Miguel Graça, Hermann Ney
This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, w…
Full-Sum Decoding for Hybrid HMM based Speech Recognition using LSTM Language Model
Wei Zhou, Ralf Schlüter, Hermann Ney
In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context…
The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment
Wei Zhou, Wilfried Michel, Kazuki Irie +3
We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data augmentation using SpecAugment is su…