no code implementations • IWSLT 2016 • Wilfried Michel, Zoltán Tüske, M. Ali Basha Shaik, Ralf Schlüter, Hermann Ney
In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT-2016 evaluation campaign are described.
1 code implementation • 4 Oct 2023 • Daniel Mann, Tina Raissi, Wilfried Michel, Ralf Schlüter, Hermann Ney
We investigate recognition results and additionally Viterbi alignments of our models.
no code implementations • 22 Apr 2022 • Wei Zhou, Wilfried Michel, Ralf Schlüter, Hermann Ney
In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period.
no code implementations • 5 Nov 2021 • Mohammad Zeineldeen, Jingjing Xu, Christoph Lüscher, Wilfried Michel, Alexander Gerstenberger, Ralf Schlüter, Hermann Ney
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 different datasets.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 18 Oct 2021 • Felix Meyer, Wilfried Michel, Mohammad Zeineldeen, Ralf Schlüter, Hermann Ney
We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attentionbased encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 18 Oct 2021 • Nils-Philipp Wynands, Wilfried Michel, Jan Rosendahl, Ralf Schlüter, Hermann Ney
Lastly, it is shown that this technique can be used to effectively perform sequence discriminative training for attention-based encoder-decoder acoustic models on the LibriSpeech task.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 12 Apr 2021 • Mohammad Zeineldeen, Aleksandr Glushko, Wilfried Michel, Albert Zeyer, Ralf Schlüter, Hermann Ney
Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions.
no code implementations • 9 Apr 2021 • Peter Vieting, Christoph Lüscher, Wilfried Michel, Ralf Schlüter, Hermann Ney
With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
2 code implementations • 7 Apr 2021 • Albert Zeyer, André Merboldt, Wilfried Michel, Ralf Schlüter, Hermann Ney
We present our transducer model on Librispeech.
Ranked #25 on Speech Recognition on LibriSpeech test-clean (using extra training data)
no code implementations • 20 May 2020 • Wilfried Michel, Ralf Schlüter, Hermann Ney
This is compared to a global renormalization scheme which is equivalent to applying shallow fusion in training.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 2 Apr 2020 • Wei Zhou, Wilfried Michel, Kazuki Irie, Markus Kitza, Ralf Schlüter, Hermann Ney
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.
no code implementations • 1 Jul 2019 • Wilfried Michel, Ralf Schlüter, Hermann Ney
This allows for a direct comparison of lattice-based and lattice-free sequence discriminative training criteria such as MMI and sMBR, both using the same language model during training.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
2 code implementations • 8 May 2019 • Christoph Lüscher, Eugen Beck, Kazuki Irie, Markus Kitza, Wilfried Michel, Albert Zeyer, Ralf Schlüter, Hermann Ney
To the best knowledge of the authors, the results obtained when training on the full LibriSpeech training set, are the best published currently, both for the hybrid DNN/HMM and the attention-based systems.
Ranked #24 on Speech Recognition on LibriSpeech test-other
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3