What is Speaker Diarization and How Does it Work?

Speaker Diarization is a technique used in Automatic Speech Recognition (ASR) to identify the number of speakers in an audio file and assign words spoken by each speaker accurately. It involves breaking down the audio file into utterances, converting them into embeddings using deep learning models, clustering these embeddings based on similarity, and finally labeling each word with a speaker label. This technology is useful for making transcriptions more readable and meaningful, as well as for analytical purposes such as identifying patterns or trends among individual speakers. However, current limitations include the inability to work in real-time and decreased accuracy when dealing with short speaker talk times, energetic conversations, or significant background noise.