42 citations · 122 across the 39 of their papers we have counts for
10 papers · 1 filter
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation
Zhong Zhou, Lori Levin, David R. Mortensen +1
We demonstrate a new approach to Neural Machine Translation (NMT) for low-resource languages using a ubiquitous linguistic resource, Interlinear Glossed Text (IGT). IGT represents…
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation
Thai-Son Nguyen, Sebastian Stueker, Jan Niehues +1
Sequence-to-Sequence (S2S) models recently started to show state-of-the-art performance for automatic speech recognition (ASR). With these large and deep models overfitting remains…
Incremental processing of noisy user utterances in the spoken language understanding task
Stefan Constantin, Jan Niehues, Alex Waibel
The state-of-the-art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time. One major challenge f…
Improving Zero-shot Translation with Language-Independent Constraints
Ngoc-Quan Pham, Jan Niehues, Thanh-Le Ha +1
An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving t…
Self-Attentional Models for Lattice Inputs
Matthias Sperber, Graham Neubig, Ngoc-Quan Pham +1
Lattices are an efficient and effective method to encode ambiguity of upstream systems in natural language processing tasks, for example to compactly capture multiple speech recogn…
Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Elizabeth Salesky, Matthias Sperber, Alex Waibel
Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies. With the rise of end-to-end speech tra…
Very Deep Self-Attention Networks for End-to-End Speech Recognition
Ngoc-Quan Pham, Thai-Son Nguyen, Jan Niehues +3
Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community. While previous architecture choices revolve arou…
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
Matthias Sperber, Graham Neubig, Jan Niehues +1
Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation…
Learning Shared Encoding Representation for End-to-End Speech Recognition Models
Thai-Son Nguyen, Sebastian Stueker, Alex Waibel
In this work, we learn a shared encoding representation for a multi-task neural network model optimized with connectionist temporal classification (CTC) and conventional framewise…
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models
Thai-Son Nguyen, Sebastian Stueker, Alex Waibel
Acoustic-to-word (A2W) models that allow direct mapping from acoustic signals to word sequences are an appealing approach to end-to-end automatic speech recognition due to their si…