FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario (2024.lrec-main)
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Andres Pineiro-Martin, Carmen Garcia-Mateo, Laura Docio-Fernandez, Maria del Carmen Lopez-Perez, Jose Gandarela-Rodriguez
| Challenge: | End-to-end (E2E) Spoken Language Understanding systems extract structured information from speech signals using a single model. |
| Approach: | They propose to use a dataset to extract structured information from speech signals . they define splits for noisy audio, hesitant audio and audio where sentence has changed . |
| Outcome: | The proposed model exploits acoustic information and avoids cascading errors . falAI dataset is the largest public SLU dataset in Galician and first to be obtained in low-resource scenario. |
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