标题： Kanade：一种用于口语语言建模的简单解耦分词器 显示英文标题

标题： Kanade: A Simple Disentangled Tokenizer for Spoken Language Modeling

摘要： 一个好的语言模型从一个好的分词器开始。 分词对于语音建模尤其重要，因为必须处理混合语言和非语言信息的连续信号。 一个语音分词器应该提取语音和语调，抑制像说话人身份这样的语言无关信息，并实现高质量的合成。 我们提出了Kanade，一种单层解耦的语音分词器，实现了这一理想。 Kanade将声学常量分离出来，创建了一个捕捉丰富语音和语调的单一标记流。 它不需要现有解耦编解码器通常依赖的辅助方法。 实验表明，Kanade在说话人解耦和词汇可用性方面达到了最先进的水平，同时保持了出色的重建质量。 显示英文摘要

摘要： A good language model starts with a good tokenizer. Tokenization is especially important for speech modeling, which must handle continuous signals that mix linguistic and non-linguistic information. A speech tokenizer should extract phonetics and prosody, suppress linguistically irrelevant information like speaker identity, and enable high-quality synthesis. We present Kanade, a single-layer disentangled speech tokenizer that realizes this ideal. Kanade separates out acoustic constants to create a single stream of tokens that captures rich phonetics and prosody. It does so without the need for auxiliary methods that existing disentangled codecs often rely on. Experiments show that Kanade achieves state-of-the-art speaker disentanglement and lexical availability, while maintaining excellent reconstruction quality.