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。

神经语音识别模型对音位同化的感知

Perception of Phonological Assimilation by Neural Speech Recognition  Models

Transformations based on domain expertise (expert transformations), such as
random-resized-crop and color-jitter, have proven critical to the success of
contrastive learning techniques such as SimCLR. Recently, several attempts have
been made to replace such domain-specific, human-designed transformations with
generated views that are learned. However for imagery data, so far none of
these view-generation methods has been able to outperform expert
transformations. In this work, we tackle a different question: instead of
replacing expert transformations with generated views, can we constructively
assimilate generated views with expert transformations? We answer this question
in the affirmative and propose a view generation method and a simple, effective
assimilation method that together improve the state-of-the-art by up to ~3.6%
on three different datasets. Importantly, we conduct a detailed empirical study
that systematically analyzes a range of view generation and assimilation
methods and provides a holistic picture of the efficacy of learned views in
contrastive representation learning.

本文提出了一种视图生成方法和简单有效的融合方法，将生成视图与人工设计的专家变换结合起来，并对比对比表示学习中学习视图的有效性进行了详细的实证研究。在三个不同的数据集上，该方法使最先进技术性能提高了约 3.6%。