no code implementations • EAMT 2022 • Mattia Di Gangi, Nick Rossenbach, Alejandro Pérez, Parnia Bahar, Eugen Beck, Patrick Wilken, Evgeny Matusov
The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized.
no code implementations • 28 May 2023 • Wei Zhou, Eugen Beck, Simon Berger, Ralf Schlüter, Hermann Ney
Modern public ASR tools usually provide rich support for training various sequence-to-sequence (S2S) models, but rather simple support for decoding open-vocabulary scenarios only.
no code implementations • 15 May 2020 • Tina Raissi, Eugen Beck, Ralf Schlüter, Hermann Ney
In this work, we address a direct phonetic context modeling for the hybrid deep neural network (DNN)/HMM, that does not build on any phone clustering algorithm for the determination of the HMM state inventory.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 1 Jul 2019 • Eugen Beck, Wei Zhou, Ralf Schlüter, Hermann Ney
LSTM based language models are an important part of modern LVCSR systems as they significantly improve performance over traditional backoff language models.
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
no code implementations • EMNLP 2018 • Jan-Thorsten Peter, Eugen Beck, Hermann Ney
Training and testing many possible parameters or model architectures of state-of-the-art machine translation or automatic speech recognition system is a cumbersome task.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3