Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios

• URL: http://arxiv.org/abs/2012.00876v1
• Date: Tue, 1 Dec 2020 22:44:42 GMT
• Title: Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios
• Authors: Peter Wu, Yifan Zhong, Alan W Black
• Abstract summary: 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.
• Score: 48.57072884674938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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