speakerlabels

Speakerlabels refer to tokens within a transcript or captioning system that indicate which person is speaking a given segment. They are used to organize dialogue, mark turns, and help readers or machines associate utterances with speakers in multi-person conversations. Labels can be anonymous, such as S1, S2, or Speaker A, or tied to real names in a project with appropriate permissions.

In linguistic research and transcription, speakerlabels help create structured corpora for analysis of dialect, discourse, and interaction patterns. Transcriptions may assign a new label whenever the speaker changes, and may also include timing information to align labels with audio. Common labeling schemes include sequential identifiers (Speaker 1, Speaker 2) or named identifiers (Person A, Interviewer, Interviewee). Consistency within a dataset is important to maintain interpretability.

In subtitling and captioning, speakerlabels enhance readability when multiple speakers appear on screen. They can be shown as on-screen identifiers or embedded in the caption text, for example, Speaker A: Hello. Some standards require brackets, dashes, or color coding to differentiate speakers, and platform guidelines vary in how and when to display labels.

In automatic speech recognition and diarization, speakerlabels originate from the diarization process, which assigns a label to each speech segment corresponding to a distinct speaker. These labels may be stable across a recording or reset between sessions. Formats such as RTTM or CTM encode speaker_id fields, while transcript formats may include inline labels.

Challenges include speaker aliasing (same person labeled differently) and speaker mix-ups during rapid turn-taking. Privacy considerations may also drive the use of anonymized labels. See also diarization, transcription, and captioning.