42 citations · 122 across the 39 of their papers we have counts for
48 papers
Incremental Blockwise Beam Search for Simultaneous Speech Translation with Controllable Quality-Latency Tradeoff
Peter Polák, Brian Yan, Shinji Watanabe +2
Blockwise self-attentional encoder models have recently emerged as one promising end-to-end approach to simultaneous speech translation. These models employ a blockwise beam search…
Convoifilter: A case study of doing cocktail party speech recognition
Thai-Binh Nguyen, Alexander Waibel
This paper presents an end-to-end model designed to improve automatic speech recognition (ASR) for a particular speaker in a crowded, noisy environment. The model utilizes a single…
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Christian Huber, Tu Anh Dinh, Carlos Mullov +10
The challenge of low-latency speech translation has recently draw significant interest in the research community as shown by several publications and shared tasks. Therefore, it is…
KIT's Multilingual Speech Translation System for IWSLT 2023
Danni Liu, Thai Binh Nguyen, Sai Koneru +7
Many existing speech translation benchmarks focus on native-English speech in high-quality recording conditions, which often do not match the conditions in real-life use-cases. In…
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation
Christian Huber, Enes Yavuz Ugan, Alexander Waibel
We propose a) a Language Agnostic end-to-end Speech Translation model (LAST), and b) a data augmentation strategy to increase code-switching (CS) performance. With increasing globa…
Towards continually learning new languages
Ngoc-Quan Pham, Jan Niehues, Alexander Waibel
Multilingual speech recognition with neural networks is often implemented with batch-learning, when all of the languages are available before training. An ability to add new langua…
Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition
Enes Yavuz Ugan, Christian Huber, Juan Hussain +1
Code-Switching (CS) is referred to the phenomenon of alternately using words and phrases from different languages. While today's neural end-to-end (E2E) models deliver state-of-the…
Adaptive multilingual speech recognition with pretrained models
Ngoc-Quan Pham, Alex Waibel, Jan Niehues
Multilingual speech recognition with supervised learning has achieved great results as reflected in recent research. With the development of pretraining methods on audio and text d…
CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022
Peter Polák, Ngoc-Quan Ngoc, Tuan-Nam Nguyen +5
In this paper, we describe our submission to the Simultaneous Speech Translation at IWSLT 2022. We explore strategies to utilize an offline model in a simultaneous setting without…
Short-Term Word-Learning in a Dynamically Changing Environment
Christian Huber, Rishu Kumar, Ondřej Bojar +1
Neural sequence-to-sequence automatic speech recognition (ASR) systems are in principle open vocabulary systems, when using appropriate modeling units. In practice, however, they o…
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages
Zhong Zhou, Alex Waibel
We translate a closed text that is known in advance and available in many languages into a new and severely low resource language. Most human translation efforts adopt a portion-ba…
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition
Christian Huber, Juan Hussain, Sebastian Stüker +1
Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When using appropriate modeling units, e.g., byte-pair encoded char…
Efficient Weight factorization for Multilingual Speech Recognition
Ngoc-Quan Pham, Tuan-Nam Nguyen, Sebastian Stueker +1
End-to-end multilingual speech recognition involves using a single model training on a compositional speech corpus including many languages, resulting in a single neural network to…
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings
Carlos Mullov, Ngoc-Quan Pham, Alexander Waibel
In this work we look into adding a new language to a multilingual NMT system in an unsupervised fashion. Under the utilization of pre-trained cross-lingual word embeddings we seek…
Relative Positional Encoding for Speech Recognition and Direct Translation
Ngoc-Quan Pham, Thanh-Le Ha, Tuan-Nam Nguyen +5
Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for…
Low Latency ASR for Simultaneous Speech Translation
Thai Son Nguyen, Jan Niehues, Eunah Cho +6
User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal. We therefore have worked on several techniques f…
Toward Cross-Domain Speech Recognition with End-to-End Models
Thai-Son Nguyen, Sebastian Stüker, Alex Waibel
In the area of multi-domain speech recognition, research in the past focused on hybrid acoustic models to build cross-domain and domain-invariant speech recognition systems. In thi…
High Performance Sequence-to-Sequence Model for Streaming Speech Recognition
Thai-Son Nguyen, Ngoc-Quan Pham, Sebastian Stueker +1
Recently sequence-to-sequence models have started to achieve state-of-the-art performance on standard speech recognition tasks when processing audio data in batch mode, i.e., the c…
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…