Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation

Zhen Ye; Xu Tan; Yiming Li; Guangyan Zhang; Chimin Chan; Haohe Liu; Zhengxi Liu; Hongzhan Lin; Zheqi Dai; Xinshen Zhang; Peiwen Sun; Qiuqiang Kong; Wei Xue

arXiv · 2606.12199

Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation

Zhen Ye·Xu Tan·Yiming Li·Guangyan Zhang·Chimin Chan·Haohe Liu·Zhengxi Liu·Hongzhan Lin·Zheqi Dai·Xinshen Zhang·Peiwen Sun·Qiuqiang Kong·Wei Xue

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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 rates feasible, we introduce factorized FSQ and a lightweight non-autoregressive audio LM head, scaling capacity to nearly 300\,bits/frame without sacrificing efficient prediction. With the bottleneck removed, we sweep frame rates (50$\rightarrow$2.08\,Hz) and alignment depth, and observe a consistent best regime for speech QA at 4.17\,Hz with intermediate-layer representation alignment.

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Zhen Ye, Xu Tan, Yiming Li, Guangyan Zhang, Chimin Chan, Haohe Liu, Zhengxi Liu, Hongzhan Lin, Zheqi Dai, Xinshen Zhang, Peiwen Sun, Qiuqiang Kong, Wei Xue. 2026-06-10. Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation. https://arxiv.org/abs/2606.12199

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