# Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios

arXiv: https://arxiv.org/abs/2012.00876 · HTML: https://mlsanity.com/paper/2012.00876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity](https://mlsanity.com/paper/2111.01326/) (2021)
- [Can the Language of the Collation be Translated into the Language of the Stemma? Using Machine Translation for Witness Localization](https://mlsanity.com/paper/2206.05603/) (2022)

## Abstract (arXiv)

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.