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Speech or text representation generated by pre-trained models containsmodal-specific information that could be combined for benefiting spokenlanguage understanding (SLU) tasks. In this work, we propose a novelpre-training paradigm termed Continuous Integrate-and-Fire Pre-Training(CIF-PT). It relies on a simple but effective frame-to-token alignment:continuous integrate-and-fire (CIF) to bridge the representations betweenspeech and text. It jointly performs speech-to-text training and language modeldistillation through CIF as the pre-training (PT). Evaluated on SLU benchmarkSLURP dataset, CIF-PT outperforms the state-of-the-art model by 1.94% ofaccuracy and 2.71% of SLU-F1 on the tasks of intent classification and slotfilling, respectively. We also observe the cross-modal representation extractedby CIF-PT obtains better performance than other neural interfaces for the tasksof SLU, including the dominant speech representation learned fromself-supervised pre-training.

CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training