FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario
Andres Pineiro-Martin, Carmen Garcia-Mateo, Laura Docio-Fernandez, Maria del Carmen Lopez-Perez, Jose Gandarela-Rodriguez
Abstract
End-to-end (E2E) Spoken Language Understanding (SLU) systems infer structured information directly from the speech signal using a single model. Due to the success of virtual assistants and the increasing demand for speech interfaces, these architectures are being actively researched for their potential to improve system performance by exploiting acoustic information and avoiding the cascading errors of traditional architectures. However, these systems require large amounts of specific, well-labelled speech data for training, which is expensive to obtain even in English, where the number of public audio datasets for SLU is limited. In this paper, we release the FalAI dataset, the largest public SLU dataset in terms of hours (250 hours), recordings (260,000) and participants (over 10,000), which is also the first SLU dataset in Galician and the first to be obtained in a low-resource scenario. Furthermore, we present new measures of complexity for the text corpora, the strategies followed for the design, collection and validation of the dataset, and we define splits for noisy audio, hesitant audio and audio where the sentence has changed but the structured information is preserved. These novel splits provide a unique resource for testing SLU systems in challenging, real-world scenarios.- Anthology ID:
- 2024.lrec-main.624
- 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:
- 7107–7116
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.624
- DOI:
- Cite (ACL):
- Andres Pineiro-Martin, Carmen Garcia-Mateo, Laura Docio-Fernandez, Maria del Carmen Lopez-Perez, and Jose Gandarela-Rodriguez. 2024. FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7107–7116, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario (Pineiro-Martin et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.lrec-main.624.pdf