Human listeners effortlessly compensate for phonological changes during
speech perception, often unconsciously inferring the intended sounds. For
example, listeners infer the underlying /n/ when hearing an utterance such as
"clea[m] pan", where [m] arises from place assimilation to the following labial
[p]. This article explores how the neural speech recognition model Wav2Vec2
perceives assimilated sounds, and identifies the linguistic knowledge that is
implemented by the model to compensate for assimilation during Automatic Speech
Recognition (ASR). Using psycholinguistic stimuli, we systematically analyze
how various linguistic context cues influence compensation patterns in the
model's output. Complementing these behavioral experiments, our probing
experiments indicate that the model shifts its interpretation of assimilated
sounds from their acoustic form to their underlying form in its final layers.
Finally, our causal intervention experiments suggest that the model relies on
minimal phonological context cues to accomplish this shift. These findings
represent a step towards better understanding the similarities and differences
in phonological processing between neural ASR models and humans.

本研究探讨了神经语音识别模型 Wav2Vec2 如何感知同化声音，并确定模型实现同化补偿的语言知识。通过心理语言学刺激和分析，我们发现不同的语言环境线索如何影响模型输出的补偿模式。我们的实验结果表明，模型在最后的层次将同化的声音从声学形式转变为其基本形式。此外，我们的因果干预实验表明，模型依赖最少的语音环境线索来实现这种转变。这些发现为更好地理解神经自动语音识别模型和人类之间的语音处理相似性和差异性迈出了一步。