TESU-LLM: Training Speech-LLMs Without Speech via Unified Encoder Alignment

• URL: http://arxiv.org/abs/2506.06343v1
• Date: Sun, 01 Jun 2025 09:27:55 GMT
• Title: TESU-LLM: Training Speech-LLMs Without Speech via Unified Encoder Alignment
• Authors: Taesoo Kim, Jong Hwan Ko,
• Abstract summary: We present textbfTESU-LLM, a novel framework that enables training speech-capable language models using only text data.<n>Our key insight is to leverage a unified encoder that maps semantically equivalent text and speech inputs to a shared latent space.<n>Despite being trained exclusively on text, TESU-LLM achieves strong performance on various speech-related benchmarks.
• Score: 15.899112804399193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in speech-enabled language models have shown promising results in building intelligent voice assistants. However, most existing approaches rely on large-scale paired speech-text data and extensive computational resources, which pose challenges in terms of scalability and accessibility. In this paper, we present \textbf{TESU-LLM}, a novel framework that enables training speech-capable language models using only text data. Our key insight is to leverage a unified encoder that maps semantically equivalent text and speech inputs to a shared latent space. By aligning the encoder output with the embedding space of a LLM via a lightweight projection network, we enable the model to generalize from text-only supervision to speech-based inference. Despite being trained exclusively on text, TESU-LLM achieves strong performance on various speech-related benchmarks, comparable to baseline methods trained with large-scale multimodal datasets and substantial computational resources. These results highlight the effectiveness and efficiency of our approach, offering a scalable path toward building speech LLMs without speech data.

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