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.
@article{arxiv.2012.00876,
title = {Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios},
author = {Peter Wu and Yifan Zhong and Alan W Black},
journal= {arXiv preprint arXiv:2012.00876},
year = {2021}
}