Abstract
Defining training, development and test set partitions for speech corpora is usually accomplished by hand. However, for the dataset under investigation, which contains a large number of speakers, eight different languages and code-switching between all the languages, this style of partitioning is not feasible. Therefore, we view the partitioning task as a resource allocation problem and propose to solve it automatically and optimally by the application of mixed-integer linear programming. Using this approach, we are able to partition a new 41.6-hour multilingual corpus of code-switched speech into training, development and testing partitions while maintaining a fixed number of speakers and a specific amount of code-switched speech in the development and test partitions. For this newly partitioned corpus, we present baseline speech recognition results using a state-of-the-art multilingual transformer model (Wav2Vec2-XLS-R) and show that the exclusion of very short utterances (<1s) results in substantially improved speech recognition performance.- Anthology ID:
- 2024.lrec-main.174
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 1944–1952
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.174
- DOI:
- Cite (ACL):
- Joshua Miles Jansen van Vüren, Febe de Wet, and Thomas Niesler. 2024. Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1944–1952, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming (Jansen van Vüren et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.174.pdf