  In recent years, there has been an increasing focus on user convenience,
leading to increased interest in text-based keyword enrollment systems for
keyword spotting (KWS). Since the system utilizes text input during the
enrollment phase and audio input during actual usage, we call this task
audio-text based KWS. To enable this task, both acoustic and text encoders are
typically trained using deep metric learning loss functions, such as triplet-
and proxy-based losses. This study aims to improve existing methods by
leveraging the structural relations within acoustic embeddings and within text
embeddings. Unlike previous studies that only compare acoustic and text
embeddings on a point-to-point basis, our approach focuses on the relational
structures within the embedding space by introducing the concept of Relational
Proxy Loss (RPL). By incorporating RPL, we demonstrated improved performance on
the Wall Street Journal (WSJ) corpus.
