Call Segmentation

Call segmentation aims to automatically divide audio recordings of phone conversations into meaningful segments, often based on topic or speaker changes. Current research focuses on improving segmentation accuracy using various approaches, including hidden Markov models incorporating self-supervised learned representations, and novel methods leveraging large language models to generate synthetic data for training and topic extraction. These advancements are crucial for efficient analysis of large volumes of call data across diverse fields like customer service and sales, enabling improved insights and automation of tasks like call summarization and sentiment analysis.