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

• URL: http://arxiv.org/abs/2606.25444v1
• Date: Wed, 24 Jun 2026 06:15:18 GMT
• Title: Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?
• Abstract summary: Speech translation provides a principled way to align the encoder's language-specific representations with the LLM's shared space.<n>We experimentally evaluate incorporating the impact of translation objectives into speech encoder pre-training.<n>Our results demonstrate that translation-enhanced pre-training improves cross-modal integration and leads to superior performance across downstream Speech LLM tasks.
• Score: 11.573851336720535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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