Word Embeddings are numerical vector representations of individual words that capture their semantic relationships, allowing AI models to understand similarities and contextual meaning.
Voice AI platforms use Word Embeddings for intent detection, semantic search, language understanding, text classification, and recommendation systems. Although newer models often use contextual embeddings, word embeddings remain a foundational AI concept.