Deep acoustic models represent linguistic information based on massive
amounts of data. Unfortunately, for regional languages and dialects such
resources are mostly not available. However, deep acoustic models might have
learned linguistic information that transfers to low-resource languages. In
this study, we evaluate whether this is the case through the task of
distinguishing low-resource (Dutch) regional varieties. By extracting
embeddings from the hidden layers of various wav2vec 2.0 models (including new
models which are pre-trained and/or fine-tuned on Dutch) and using dynamic time
warping, we compute pairwise pronunciation differences averaged over 10 words
for over 100 individual dialects from four (regional) languages. We then
cluster the resulting difference matrix in four groups and compare these to a
gold standard, and a partitioning on the basis of comparing phonetic
transcriptions. Our results show that acoustic models outperform the
(traditional) transcription-based approach without requiring phonetic
transcriptions, with the best performance achieved by the multilingual XLSR-53
model fine-tuned on Dutch. On the basis of only six seconds of speech, the
resulting clustering closely matches the gold standard.

本研究通过 wav2vec 2.0 模型中提取的嵌入来区分多达 100 种荷兰四种方言的发音差异，结果表明，基于声学模型的方法优于基于电话转录的方法，且 XLSR-53 模型在荷兰方言的微调后表现最佳，仅仅利用六秒的语音，即可得到与实际情况吻合的聚类。