282 citations · 1.2k across the 80 of their papers we have counts for
13 papers · 1 filter
Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers
Zijian Yang, Wei Zhou, Ralf Schlüter +1
Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obta…
Does Joint Training Really Help Cascaded Speech Translation?
Viet Anh Khoa Tran, David Thulke, Yingbo Gao +2
Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results. However, fundamental…
Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Token
Baohao Liao, David Thulke, Sanjika Hewavitharana +2
The pre-training of masked language models (MLMs) consumes massive computation to achieve good results on downstream NLP tasks, resulting in a large carbon footprint. In the vanill…
Enhancing and Adversarial: Improve ASR with Speaker Labels
Wei Zhou, Haotian Wu, Jingjing Xu +4
ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain va…
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model
Nico Daheim, David Thulke, Christian Dugast +1
In this work, we present a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes theorem. One component is a tradition…
Monotonic segmental attention for automatic speech recognition
Albert Zeyer, Robin Schmitt, Wei Zhou +2
We introduce a novel segmental-attention model for automatic speech recognition. We restrict the decoder attention to segments to avoid quadratic runtime of global attention, bette…
Is Encoder-Decoder Redundant for Neural Machine Translation?
Yingbo Gao, Christian Herold, Zijian Yang +1
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks. For machine translation, despite the evolution from long short-term memory networks to Trans…
Revisiting Checkpoint Averaging for Neural Machine Translation
Yingbo Gao, Christian Herold, Zijian Yang +1
Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models. The calculation is cheap to perform and the fact that…
HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch
Tina Raissi, Wei Zhou, Simon Berger +2
In this work, we compare from-scratch sequence-level cross-entropy (full-sum) training of Hidden Markov Model (HMM) and Connectionist Temporal Classification (CTC) topologies for a…
Efficient Utilization of Large Pre-Trained Models for Low Resource ASR
Peter Vieting, Christoph Lüscher, Julian Dierkes +2
Unsupervised representation learning has recently helped automatic speech recognition (ASR) to tackle tasks with limited labeled data. Following this, hardware limitations and appl…
Improving the Training Recipe for a Robust Conformer-based Hybrid Model
Mohammad Zeineldeen, Jingjing Xu, Christoph Lüscher +2
Speaker adaptation is important to build robust automatic speech recognition (ASR) systems. In this work, we investigate various methods for speaker adaptive training (SAT) based o…
Efficient Training of Neural Transducer for Speech Recognition
Wei Zhou, Wilfried Michel, Ralf Schlüter +1
As one of the most popular sequence-to-sequence modeling approaches for speech recognition, the RNN-Transducer has achieved evolving performance with more and more sophisticated ne…
Improving Factored Hybrid HMM Acoustic Modeling without State Tying
Tina Raissi, Eugen Beck, Ralf Schlüter +1
In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored…