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.