标题： 分析神经音频编码器的语言 显示英文标题

标题： Analysing the Language of Neural Audio Codecs

摘要： 本研究对神经音频编解码器（NACs）的统计和语言属性进行了比较分析。 我们研究了各种NAC模型生成的离散语音标记，考察它们是否遵循如齐夫定律和海普斯定律等语言统计规律，以及它们的熵和冗余度。 为了评估这些标记级别的属性如何与合成语音中的语义和声学保留相关，我们使用自动语音识别的错误率来评估可理解性，并使用UTMOS分数来评估质量。 我们的结果表明，NAC标记，特别是3-gram，表现出类似语言的统计模式。 此外，这些属性以及信息内容的度量被发现与语音识别和重合成任务中性能的提升有关。 这些发现为NAC标记序列的结构提供了见解，并有助于设计更有效的生成语音模型。 显示英文摘要

摘要： This study presents a comparative analysis of the statistical and linguistic properties of neural audio codecs (NACs). We investigate discrete speech tokens produced by various NAC models, examining their adherence to linguistic statistical laws such as Zipf's law and Heaps' law, as well as their entropy and redundancy. To assess how these token-level properties relate to semantic and acoustic preservation in synthesized speech, we evaluate intelligibility using error rates of automatic speech recognition, and quality using the UTMOS score. Our results reveal that NAC tokens, particularly 3-grams, exhibit language-like statistical patterns. Moreover, these properties, together with measures of information content, are found to correlate with improved performances in speech recognition and resynthesis tasks. These findings offer insights into the structure of NAC token sequences and inform the design of more effective generative speech models.