Speaker Diarization

Speaker diarization is a process in natural language processing that involves partitioning an audio stream into segments based on the identity of the speaker. It aims to answer the question, 'who spoke when?' by identifying and distinguishing between different speakers in a conversation. This technique is particularly useful in scenarios such as meeting transcriptions, broadcast news, and telephone conversations, where multiple speakers may be present. Key characteristics include the use of machine learning algorithms and audio feature extraction to improve accuracy. Common use cases include enhancing accessibility for the hearing impaired, improving customer service interactions, and analyzing social dynamics in conversations.