Interpretability research has shown that self-supervised Spoken Language
Models (SLMs) encode a wide variety of features in human speech from the
acoustic, phonetic, phonological, syntactic and semantic levels, to speaker
characteristics. The bulk of prior research on representations of phonology has
focused on segmental features such as phonemes; the encoding of suprasegmental
phonology (such as tone and stress patterns) in SLMs is not yet well
understood. Tone is a suprasegmental feature that is present in more than half
of the world's languages. This paper aims to analyze the tone encoding
capabilities of SLMs, using Mandarin and Vietnamese as case studies. We show
that SLMs encode lexical tone to a significant degree even when they are
trained on data from non-tonal languages. We further find that SLMs behave
similarly to native and non-native human participants in tone and consonant
perception studies, but they do not follow the same developmental trajectory.

自监督语言模型在编码语音的各个层面上揭示了许多特征，本文通过分析普通话和越南语来研究语言模型对音调的编码能力，发现即使在使用非语调语言的数据进行训练时，语言模型仍然在很大程度上编码了词汇音调。同时，发现语言模型在音调和辅音感知研究中表现出与母语和非母语人类参与者相似的行为，但并没有遵循相同的发展轨迹。