Investigating Decoder-only Large Language Models for Speech-to-text Translation

• URL: http://arxiv.org/abs/2407.03169v1
• Date: Wed, 3 Jul 2024 14:42:49 GMT
• Title: Investigating Decoder-only Large Language Models for Speech-to-text Translation
• Authors: Chao-Wei Huang, Hui Lu, Hongyu Gong, Hirofumi Inaguma, Ilia Kulikov, Ruslan Mavlyutov, Sravya Popuri,
• Abstract summary: Large language models (LLMs) are known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains. We propose a decoder-only architecture that enables the LLM to directly consume the encoded speech representation and generate the text translation. Our model achieves state-of-the-art performance on CoVoST 2 and FLEURS among models trained without proprietary data.
• Score: 39.17113782374464
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs), known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains, present a promising avenue for enhancing speech-related tasks. In this paper, we focus on integrating decoder-only LLMs to the task of speech-to-text translation (S2TT). We propose a decoder-only architecture that enables the LLM to directly consume the encoded speech representation and generate the text translation. Additionally, we investigate the effects of different parameter-efficient fine-tuning techniques and task formulation. Our model achieves state-of-the-art performance on CoVoST 2 and FLEURS among models trained without proprietary data. We also conduct analyses to validate the design choices of our proposed model and bring insights to the integration of LLMs to S2TT.

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