Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language

Understand

Acoustic word embedding models map variable duration speech segments to fixed dimensional vectors, enabling efficient speech search and discovery.

• Previous work explored how embeddings can be obtained in zero-resource settings where no labelled data is available in the target language.
• The current best approach uses transfer learning: a single supervised multilingual model is trained using labelled data from multiple well-resourced languages and then applied to a target zero-resource language (without fine-tuning).
• However, it is still unclear how the specific choice of training languages affect downstream performance.