Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation
arXiv:2606.12199v1 Announce Type: cross Abstract: Spoken dialogue models typically start from text LLM backbones, yet reasoning often degrades when conditioning on speech instead of text. We attribute part of this modality gap to a temporal-granularity mismatch: speech tokens are temporally redundant and far longer than text under matched semantics, diluting per-token semantic density and weakening text-native reasoning dynamics. We study speech token design as a representation selection problem and sweep frame rates under a frozen LLM backbone with a fixed information rate. To make low frame
The proliferation of advanced LLMs and multimodal AI capabilities highlights the current limitations in effectively integrating diverse data modalities, especially speech, into text-native reasoning systems.
This research addresses a fundamental challenge in AI by identifying how speech representation impacts reasoning, which can unlock more robust and human-like interaction with AI models by bridging the modality gap.
The understanding of how speech processing needs to be optimized for LLMs shifts from merely transcription to a more nuanced focus on temporal granularity and semantic density in speech tokens.
- · AI developers
- · Multimodal AI platforms
- · Speech technology companies
- · Developers of AI agents
- · Legacy speech-to-text providers (if they don't adapt)
- · AI models reliant on unoptimized speech inputs
Improved performance and reliability of AI systems processing spoken language for complex reasoning tasks.
Accelerated development of advanced conversational AI and voice-controlled interfaces that can handle sophisticated user queries.
Enhanced AI 'agency' in real-world environments where spoken interaction is paramount, leading to more pervasive and intuitive AI integration.
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Read at arXiv cs.CL