  Existing multilingual speech NLP works focus on a relatively small subset of
languages, and thus current linguistic understanding of languages predominantly
stems from classical approaches. In this work, we propose a method to analyze
language similarity using deep learning. Namely, we train a model on the
Wilderness dataset and investigate how its latent space compares with classical
language family findings. Our approach provides a new direction for
cross-lingual data augmentation in any speech-based NLP task.
