Joint Multimodal Contrastive Learning for Robust Spoken Term Detection and Keyword Spotting

This article likely presents a novel approach to spoken term detection and keyword spotting using joint multimodal contrastive learning. The focus is on improving robustness, suggesting the methods are designed to perform well under noisy or varied conditions. The use of 'joint multimodal' implies the integration of different data modalities (e.g., audio and text) for enhanced performance. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results of the proposed approach.