BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation

• URL: http://arxiv.org/abs/2405.19041v1
• Date: Wed, 29 May 2024 12:32:08 GMT
• Title: BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation
• Authors: Chen Wang, Minpeng Liao, Zhongqiang Huang, Jiajun Zhang,
• Abstract summary: We introduce BLSP-KD, a novel approach for Bootstrapping Language-Speech Pretraining via Knowledge Distillation. It optimize speech-text alignment by minimizing the divergence between the LLM's next-token prediction distributions for speech and text inputs. It also employs a continuous-integrate-andfire strategy to segment speech into tokens that correspond one-to-one with text tokens, enabling fine-grained alignment.
• Score: 18.329192763760034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent end-to-end approaches have shown promise in extending large language models (LLMs) to speech inputs, but face limitations in directly assessing and optimizing alignment quality and fail to achieve fine-grained alignment due to speech-text length mismatch. We introduce BLSP-KD, a novel approach for Bootstrapping Language-Speech Pretraining via Knowledge Distillation, which addresses these limitations through two key techniques. First, it optimizes speech-text alignment by minimizing the divergence between the LLM's next-token prediction distributions for speech and text inputs using knowledge distillation. Second, it employs a continuous-integrate-andfire strategy to segment speech into tokens that correspond one-to-one with text tokens, enabling fine-grained alignment. We also introduce Partial LoRA (PLoRA), a new adaptation method supporting LLM finetuning for speech inputs under knowledge distillation. Quantitative evaluation shows that BLSP-KD outperforms previous end-to-end baselines and cascaded systems with comparable scale of parameters, facilitating general instruction-following capabilities for LLMs with speech inputs. This approach provides new possibilities for extending LLMs to spoken language interactions.

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