282 citations · 700 across the 18 of their papers we have counts for
6 papers · 1 filter
Dynamic Encoder Size Based on Data-Driven Layer-wise Pruning for Speech Recognition
Jingjing Xu, Wei Zhou, Zijian Yang +2
Varying-size models are often required to deploy ASR systems under different hardware and/or application constraints such as memory and latency. To avoid redundant training and opt…
On Architectures and Training for Raw Waveform Feature Extraction in ASR
Peter Vieting, Christoph Lüscher, Wilfried Michel +2
With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly bas…
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…
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…
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR
Wilfried Michel, Ralf Schlüter, Hermann Ney
Sequence discriminative training criteria have long been a standard tool in automatic speech recognition for improving the performance of acoustic models over their maximum likelih…
LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring
Eugen Beck, Wei Zhou, Ralf Schlüter +1
LSTM based language models are an important part of modern LVCSR systems as they significantly improve performance over traditional backoff language models. Incorporating them effi…