142 citations · 163 across the 11 of their papers we have counts for
13 papers
AppTek Call-Center Dialogues: A Multi-Accent Long-Form Benchmark for English ASR
Eugen Beck, Sarah Beranek, Uma Moothiringote +4
Evaluating English ASR systems for conversational AI applications remains difficult, as many publicly available corpora are either pre-segmented into short segments, consist of rea…
End-to-End Training of a Neural HMM with Label and Transition Probabilities
Daniel Mann, Tina Raissi, Wilfried Michel +2
We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modele…
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…
Conformer-based Hybrid ASR System for Switchboard Dataset
Mohammad Zeineldeen, Jingjing Xu, Christoph Lüscher +4
The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on di…
Automatic Learning of Subword Dependent Model Scales
Felix Meyer, Wilfried Michel, Mohammad Zeineldeen +2
To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior cor…
Efficient Sequence Training of Attention Models using Approximative Recombination
Nils-Philipp Wynands, Wilfried Michel, Jan Rosendahl +2
Sequence discriminative training is a great tool to improve the performance of an automatic speech recognition system. It does, however, necessitate a sum over all possible word se…
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models
Mohammad Zeineldeen, Aleksandr Glushko, Wilfried Michel +3
Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions. The integration with an external LM trained on much m…
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…
Librispeech Transducer Model with Internal Language Model Prior Correction
Albert Zeyer, André Merboldt, Wilfried Michel +2
We present our transducer model on Librispeech. We study variants to include an external language model (LM) with shallow fusion and subtract an estimated internal LM. This is just…
Early Stage LM Integration Using Local and Global Log-Linear Combination
Wilfried Michel, Ralf Schlüter, Hermann Ney
Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) for the task…
The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment
Wei Zhou, Wilfried Michel, Kazuki Irie +3
We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data augmentation using SpecAugment is su…
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…
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
Christoph Lüscher, Eugen Beck, Kazuki Irie +5
We present state-of-the-art automatic speech recognition (ASR) systems employing a standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder design for th…