173 citations · 306 across the 58 of their papers we have counts for
9 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…
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…
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…
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…
Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech
Christoph Lüscher, Mohammad Zeineldeen, Zijian Yang +6
Language barriers present a great challenge in our increasingly connected and global world. Especially within the medical domain, e.g. hospital or emergency room, communication dif…
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…