Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?

Tomoya Mizumoto; Yusuke Fujita

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arXiv · 2606.25444

Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?

Tomoya Mizumoto·Yusuke Fujita

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Abstract

Connecting a pre-trained speech encoder to a Large Language Model (LLM) is the standard architecture for building Speech LLMs. However, a structural misalignment exists between the encoder and the LLM. Unlike encoders based on automatic speech recognition, which often produce representations in separate language-specific spaces, LLMs operate within a unified language-agnostic space. A mechanism is required to align the encoder's language-specific representations with the LLM's shared space. We argue that speech translation provides a principled way to achieve this. Unlike monolingual transcription, translation requires the model to bridge different languages and learn language-agnostic representations. We experimentally evaluate the impact of incorporating translation objectives into speech encoder pre-training. Our results demonstrate that translation-enhanced pre-training improves cross-modal integration and leads to superior performance across downstream Speech LLM tasks.

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Tomoya Mizumoto, Yusuke Fujita. 2026-06-24. Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?. https://arxiv.org/abs/2606.25444

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