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

• URL: http://arxiv.org/abs/2309.02780v2
• Date: Mon, 11 Sep 2023 09:35:14 GMT
• Title: GRASS: Unified Generation Model for Speech-to-Semantic Tasks
• Authors: Aobo Xia, Shuyu Lei, Yushu Yang, Xiang Guo and Hua Chai
• Abstract summary: We introduce a unified end-to-end (E2E) framework that generates target text conditioned on a task-related prompt for audio data. 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. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code.
• Score: 7.044414457214718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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