  In this study, we introduce a novel cross-modal retrieval task involving
speaker descriptions and their corresponding audio samples. Utilizing
pre-trained speaker and text encoders, we present a simple learning framework
based on contrastive learning. Additionally, we explore the impact of
incorporating speaker labels into the training process. Our findings establish
the effectiveness of linking speaker and text information for the task for both
English and Japanese languages, across diverse data configurations. Additional
visual analysis unveils potential nuanced associations between speaker
clustering and retrieval performance.
