Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation

• URL: http://arxiv.org/abs/2407.09886v2
• Date: Mon, 23 Sep 2024 16:45:04 GMT
• Title: Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation
• Authors: Chun-Yi Kuan, Chih-Kai Yang, Wei-Ping Huang, Ke-Han Lu, Hung-yi Lee,
• Abstract summary: Speech-Copilot is a modular framework for instruction-oriented speech-processing tasks. It builds speech processing-specific toolsets by analyzing pre-collected task instructions. It features a flexible agent based on large language models that performs tasks through program generation.
• Score: 42.55462692822432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce Speech-Copilot, a modular framework for instruction-oriented speech-processing tasks that minimizes human effort in toolset construction. Unlike end-to-end methods using large audio-language models, Speech-Copilot builds speech processing-specific toolsets by analyzing pre-collected task instructions and breaking tasks into manageable sub-tasks. It features a flexible agent based on large language models that performs tasks through program generation. Our approach achieves state-of-the-art performance on the Dynamic-SUPERB benchmark, demonstrating its effectiveness across diverse speech-processing tasks. Key contributions include: 1) developing an innovative framework for speech processing-specific toolset construction, 2) establishing a high-performing agent based on large language models, and 3) offering a new perspective on addressing challenging instruction-oriented speech-processing tasks. Without additional training processes required by end-to-end approaches, our method provides a flexible and extendable solution for a wide range of speech-processing applications.

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