Unified speech foundation models require a holistic tokenization space that is both learnable by language models and decodable into high-quality waveforms. Existing speech tokenizers, however, often fail to satisfy these requirements simultaneously, leading to increased architectural complexity and more involved training designs. We propose HoliTok, a continuous Holistic speech Tokenization model designed for unified generation-understanding modeling. HoliTok encodes 48~kHz speech into a compact 25~Hz sequence of 128-dimensional latents. It is trained with a progressive strategy that jointly preserves signal-level fidelity, incorporates semantic information, and maintains strong latent learnability. Based on this tokenization, we build a unified AR+DiT model for speech synthesis and recognition, where the same latent sequence supports both generation-specific and unified generation-understanding tasks. Experiments show that HoliTok achieves competitive reconstruction fidelity, improves generative learnability for high-quality and controllable synthesis, and, among the evaluated representations, is the only one that operates robustly in our unified generation-understanding architecture without additional optimization tricks. These results suggest that HoliTok serves as an effective speech tokenizer and a foundational representation interface for unified spoken language modeling. The code is available at: https://github.com/bovod-sjtu/HoliTok.

翻译：统一语音基础模型需要一个既能让语言模型学习、又能解码为高质量波形的整体式分词空间。然而，现有的语音分词器往往无法同时满足这些要求，从而导致架构复杂性增加以及训练设计更为繁琐。本文提出HoliTok，一种为统一生成-理解建模而设计的连续整体式语音分词模型。HoliTok将48kHz语音编码为紧凑的25Hz、128维潜在变量序列。它采用渐进式训练策略，联合保留了信号级保真度、融入了语义信息，并保持了强大的潜在可学习性。基于这种分词方式，我们构建了一个用于语音合成与识别的统一AR+DiT模型，其中相同的潜在序列既支持生成特定任务，也支持统一的生成-理解任务。实验表明，HoliTok在实现有竞争力的重建保真度的同时，提升了高质量与可控合成场景下的生成可学习性；在评估的各类表征中，它是唯一能够稳健运行于我们统一的生成-理解架构中而无需额外优化技巧的表征方法。这些结果表明，HoliTok在高保真度和表达能力方面均表现出色，可作为一种有效的语音分词器以及统一口语建模的基础表征接口。代码见：https://github.com/bovod-sjtu/HoliTok。