Application: enriching and adding structure to audiovisual data
Speaker diarization, also referred to as speaker segmentation and clustering, is an advanced process that involves partitioning an audio stream into distinct segments based on the identity of the speakers. By distinguishing between individual voices, speaker diarization provides a clearer structure to otherwise continuous and overlapping speech, making it much easier to analyze. This process is particularly useful for enhancing the readability and usability of automatic transcriptions by organizing the audio into clearly defined speaker turns, which not only improves the overall flow of the transcription but also helps in identifying who is speaking at any given moment.

A practical and impactful use of speaker diarization is its application as a 'Who's Who' in audio documents, allowing for a detailed record of 'who spoke when.' This functionality is especially valuable in contexts where knowing the speaker's identity at specific points in a conversation is critical. For instance, during a Presidential election period in France, this technology was employed to track the speaking times of political candidates. By using speaker diarization to pinpoint and separate the contributions of each speaker, it provided a valuable tool to assist human operators in accurately assessing speaking times and ensuring fair and balanced representation. This application highlights the potential of speaker diarization not just for transcription.