Internalizing ASR with Implicit Chain of Thought for Efficient Speech-to-Speech Conversational LLM

• URL: http://arxiv.org/abs/2409.17353v3
• Date: Mon, 4 Nov 2024 08:01:22 GMT
• Title: Internalizing ASR with Implicit Chain of Thought for Efficient Speech-to-Speech Conversational LLM
• Authors: Robin Shing-Hei Yuen, Timothy Tin-Long Tse, Jian Zhu,
• Abstract summary: We propose a method that implicitly internalizes ASR chain of thought into a speech LLM, enhancing its native speech understanding capabilities. Our approach reduces latency and improves the model's native understanding of speech, paving the way for more efficient and natural real-time audio interactions.
• Score: 3.6950912517562435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current speech-based LLMs are predominantly trained on extensive ASR and TTS datasets, excelling in tasks related to these domains. However, their ability to handle direct speech-to-speech conversations remains notably constrained. These models often rely on an ASR-to-TTS chain-of-thought pipeline, converting speech into text for processing before generating audio responses, which introduces latency and loses audio features. We propose a method that implicitly internalizes ASR chain of thought into a speech LLM, enhancing its native speech understanding capabilities. Our approach reduces latency and improves the model's native understanding of speech, paving the way for more efficient and natural real-time audio interactions. We also release a large-scale synthetic conversational dataset to facilitate further research.

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