Computer Science > Computation and Language
[Submitted on 6 Sep 2023 (v1), last revised 11 Sep 2023 (this version, v2)]
Title:GRASS: Unified Generation Model for Speech-to-Semantic Tasks
View PDFAbstract:This paper explores the instruction fine-tuning technique for speech-to-semantic tasks by introducing a unified end-to-end (E2E) framework that generates target text conditioned on a task-related prompt for audio data. We pre-train the model using large and diverse data, where instruction-speech pairs are constructed via a text-to-speech (TTS) system. Extensive experiments demonstrate that our proposed model achieves state-of-the-art (SOTA) results on many benchmarks covering speech named entity recognition, speech sentiment analysis, speech question answering, and more, after fine-tuning. Furthermore, the proposed model achieves competitive performance in zero-shot and few-shot scenarios. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code.
Submission history
From: Shuyu Lei [view email][v1] Wed, 6 Sep 2023 06:44:26 UTC (1,770 KB)
[v2] Mon, 11 Sep 2023 09:35:14 UTC (1,772 KB)
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