Abstract
In this paper, we investigate a multilingual approach for speech disfluency removal. A major challenge of this task comes from the costly nature of disfluency annotation. Motivated by the fact that speech disfluencies are commonly observed throughout different languages, we investigate the potential of multilingual disfluency modeling. We suggest that learning a joint representation of the disfluencies in multiple languages can be a promising solution to the data sparsity issue. In this work, we utilize a multilingual neural machine translation system, where a disfluent speech transcript is directly transformed into a cleaned up text. Disfluency removal experiments on English and German speech transcripts show that multilingual disfluency modeling outperforms the single language systems. In a following experiment, we show that the improvements are also observed in a downstream application using the disfluency-removed transcripts as input.- Anthology ID:
- 2016.iwslt-1.10
- Volume:
- Proceedings of the 13th International Conference on Spoken Language Translation
- Month:
- December 8-9
- Year:
- 2016
- Address:
- Seattle, Washington D.C
- Editors:
- Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, Rolando Cattoni, Marcello Federico
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- International Workshop on Spoken Language Translation
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2016.iwslt-1.10
- DOI:
- Cite (ACL):
- Eunah Cho, Jan Niehues, Thanh-Le Ha, and Alex Waibel. 2016. Multilingual Disfluency Removal using NMT. In Proceedings of the 13th International Conference on Spoken Language Translation, Seattle, Washington D.C. International Workshop on Spoken Language Translation.
- Cite (Informal):
- Multilingual Disfluency Removal using NMT (Cho et al., IWSLT 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2016.iwslt-1.10.pdf