Abstract
For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation. For online spoken language translation, where models need to start translating before the full utterance is spoken,most previous work has ignored the segmentation problem. In this paper, we compare various methods for improving models’ robustness towards segmentation errors and different segmentation strategies in both offline and online settings and report results on translation quality, flicker and delay. Our findings on five different language pairs show that a simple fixed-window audio segmentation can perform surprisingly well given the right conditions.- Anthology ID:
- 2022.wmt-1.13
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 203–219
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.13
- DOI:
- Bibkey:
- Cite (ACL):
- Chantal Amrhein and Barry Haddow. 2022. Don’t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 203–219, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Don’t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation (Amrhein & Haddow, WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.13.pdf
- Video:
- https://aclanthology.org/2022.wmt-1.13.mp4
Export citation
@inproceedings{amrhein-haddow-2022-dont,
title = "Don{'}t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation",
author = "Amrhein, Chantal and
Haddow, Barry",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.13",
pages = "203--219",
abstract = "For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation. For online spoken language translation, where models need to start translating before the full utterance is spoken,most previous work has ignored the segmentation problem. In this paper, we compare various methods for improving models{'} robustness towards segmentation errors and different segmentation strategies in both offline and online settings and report results on translation quality, flicker and delay. Our findings on five different language pairs show that a simple fixed-window audio segmentation can perform surprisingly well given the right conditions.",
}
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%0 Conference Proceedings %T Don’t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation %A Amrhein, Chantal %A Haddow, Barry %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F amrhein-haddow-2022-dont %X For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation. For online spoken language translation, where models need to start translating before the full utterance is spoken,most previous work has ignored the segmentation problem. In this paper, we compare various methods for improving models’ robustness towards segmentation errors and different segmentation strategies in both offline and online settings and report results on translation quality, flicker and delay. Our findings on five different language pairs show that a simple fixed-window audio segmentation can perform surprisingly well given the right conditions. %U https://aclanthology.org/2022.wmt-1.13 %P 203-219
Markdown (Informal)
[Don’t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation](https://aclanthology.org/2022.wmt-1.13) (Amrhein & Haddow, WMT 2022)
- Don’t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation (Amrhein & Haddow, WMT 2022)
ACL
- Chantal Amrhein and Barry Haddow. 2022. Don’t Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 203–219, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.