Speech-Language Models with Decoupled Tokenizers and Multi-Token Prediction

• URL: http://arxiv.org/abs/2506.12537v1
• Date: Sat, 14 Jun 2025 15:26:31 GMT
• Title: Speech-Language Models with Decoupled Tokenizers and Multi-Token Prediction
• Authors: Xiaoran Fan, Zhichao Sun, Yangfan Gao, Jingfei Xiong, Hang Yan, Yifei Cao, Jiajun Sun, Shuo Li, Zhihao Zhang, Zhiheng Xi, Yuhao Zhou, Senjie Jin, Changhao Jiang, Junjie Ye, Ming Zhang, Rui Zheng, Zhenhua Han, Yunke Zhang, Demei Yan, Shaokang Dong, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang,
• Abstract summary: Speech-language synthesis models (SLMs) offer a promising path toward unifying speech and text understanding and generation.<n>We investigate the impact of key components (i.e., speech tokenizers, speech heads, and speaker modeling) on the performance of SLMs.
• Score: 58.55905182336196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech-language models (SLMs) offer a promising path toward unifying speech and text understanding and generation. However, challenges remain in achieving effective cross-modal alignment and high-quality speech generation. In this work, we systematically investigate the impact of key components (i.e., speech tokenizers, speech heads, and speaker modeling) on the performance of LLM-centric SLMs. We compare coupled, semi-decoupled, and fully decoupled speech tokenizers under a fair SLM framework and find that decoupled tokenization significantly improves alignment and synthesis quality. To address the information density mismatch between speech and text, we introduce multi-token prediction (MTP) into SLMs, enabling each hidden state to decode multiple speech tokens. This leads to up to 12$\times$ faster decoding and a substantial drop in word error rate (from 6.07 to 3.01). Furthermore, we propose a speaker-aware generation paradigm and introduce RoleTriviaQA, a large-scale role-playing knowledge QA benchmark with diverse speaker identities. Experiments demonstrate that our methods enhance both knowledge understanding and speaker consistency.

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