Robustness of end-to-end Automatic Speech Recognition Models – A Case Study using Mozilla DeepSpeech
- Anthology ID:
- 2021.konvens-1.18
- Volume:
- Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)
- Month:
- 6--9 September
- Year:
- 2021
- Address:
- Düsseldorf, Germany
- Editors:
- Kilian Evang, Laura Kallmeyer, Rainer Osswald, Jakub Waszczuk, Torsten Zesch
- Venue:
- KONVENS
- SIG:
- Publisher:
- KONVENS 2021 Organizers
- Note:
- Pages:
- 203–207
- Language:
- URL:
- https://aclanthology.org/2021.konvens-1.18
- DOI:
- Cite (ACL):
- Aashish Agarwal and Torsten Zesch. 2021. Robustness of end-to-end Automatic Speech Recognition Models – A Case Study using Mozilla DeepSpeech. In Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021), pages 203–207, Düsseldorf, Germany. KONVENS 2021 Organizers.
- Cite (Informal):
- Robustness of end-to-end Automatic Speech Recognition Models – A Case Study using Mozilla DeepSpeech (Agarwal & Zesch, KONVENS 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.konvens-1.18.pdf