In this work, we study the features extracted by English self-supervised
learning (SSL) models in cross-lingual contexts and propose a new metric to
predict the quality of feature representations. Using automatic speech
recognition (ASR) as a downstream task, we analyze the effect of model size,
training objectives, and model architecture on the models' performance as a
feature extractor for a set of topologically diverse corpora. We develop a
novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and
synthetic information in the extracted representations using deep generalized
canonical correlation analysis. Results show the contrastive loss in the
wav2vec2.0 objective facilitates more effective cross-lingual feature
extraction. There is a positive correlation between PSR scores and ASR
performance, suggesting that phonetic information extracted by monolingual SSL
models can be used for downstream tasks in cross-lingual settings. The proposed
metric is an effective indicator of the quality of the representations and can
be useful for model selection.

通过对英文自监督学习模型在跨语言环境中提取的特征进行研究，我们提出了一种新的度量标准来预测特征表示的质量。使用自动语音识别作为下游任务，我们分析了模型大小、训练目标和模型架构对一组拓扑多样的语料库中模型作为特征提取器的性能的影响。我们开发了一种新的度量标准，即 Phonetic-Syntax Ratio (PSR)，通过深度广义典型相关分析来衡量提取表示中的音标和合成信息。结果表明，wav2vec2.0 目标中的对比损失有助于更有效的跨语言特征提取。PSR 分数与自动语音识别性能呈正相关，表明单语自监督学习模型提取的音标信息可以用于跨语言设置中的下游任务。提出的度量标准是表示质量的有效指标，可用于模型选择。