A Joint Embedding Model learns a shared vector representation for different types of data, such as speech, text, or images, allowing related information to be compared within the same embedding space.
Voice AI platforms use Joint Embedding Models for cross-modal search, semantic retrieval, multimodal AI, speaker identification, and Retrieval-Augmented Generation (RAG). Shared embeddings improve matching accuracy across different data sources.