Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings

TL;DR

This paper presents a contrastively trained transformer model that generates acoustic word embeddings for effective keyword spotting in low-resource languages, outperforming existing methods.

Contribution

The introduction of the ContrastiveTransformer, a novel encoder-only model optimized with NT-Xent loss for low-resource keyword spotting tasks.

Findings

Outperforms existing AWE methods in low-resource settings

Effective in Luganda and Bambara radio broadcasts

Shows significant improvement over DTW baseline

Abstract

We introduce a new approach, the ContrastiveTransformer, that produces acoustic word embeddings (AWEs) for the purpose of very low-resource keyword spotting. The ContrastiveTransformer, an encoder-only model, directly optimises the embedding space using normalised temperature-scaled cross entropy (NT-Xent) loss. We use this model to perform keyword spotting for radio broadcasts in Luganda and Bambara, the latter a severely under-resourced language. We compare our model to various existing AWE approaches, including those constructed from large pre-trained self-supervised models, a recurrent encoder which previously used the NT-Xent loss, and a DTW baseline. We demonstrate that the proposed contrastive transformer approach offers performance improvements over all considered existing approaches to very low-resource keyword spotting in both languages.