Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings
Myunghun Jung, Hoi Rin Kim · Interspeech 2022 · 2022
Acoustic word embeddings (AWEs) are discriminative representations of speech segments, and learned embedding space reflects the phonetic similarity between words.With multi-view learning, where text labels are considered as supplementary input, AWEs are jointly trained with acoustically grounded word embeddings (AGWEs).In this paper, we expand the multiview approach into a proxy-based framework for deep metric learning by equating AGWEs with proxies.A simple modification in computing the similarity matrix allows the general pair weighting to formulate the data-to-proxy relationship.Under the systematized framework, we propose an asymmetric-proxy loss that combines different parts of loss functions asymmetrically while keeping their merits.It follows the assumptions that the optimal function for anchor-positive pairs may differ from one for anchor-negative pairs, and a proxy may have a different impact when it substitutes for different positions in the triplet.We present comparative experiments with various proxy-based losses including our asymmetric-proxy loss, and evaluate AWEs and AGWEs for word discrimination tasks on WSJ corpus.The results demonstrate the effectiveness of the proposed method.