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This paper explores the instruction fine-tuning technique forspeech-to-semantic tasks by introducing a unified end-to-end (E2E) frameworkthat generates target text conditioned on a task-related prompt for audio data.We pre-train the model using large and diverse data, where instruction-speechpairs are constructed via a text-to-speech (TTS) system. Extensive experimentsdemonstrate that our proposed model achieves state-of-the-art (SOTA) results onmany benchmarks covering speech named entity recognition, speech sentimentanalysis, speech question answering, and more, after fine-tuning. Furthermore,the proposed model achieves competitive performance in zero-shot and few-shotscenarios. To facilitate future work on instruction fine-tuning forspeech-to-semantic tasks, we release our instruction dataset and code.

GRASS: Unified Generation Model for Speech-to-Semantic Tasks