Speaker Labels for Hinglish Interviews

For two-person interviews, use the speakers' roles or names: 'Interviewer:' and 'Guest:' for formal contexts, or first names for casual podcasts. Avoid 'Speaker 1' and 'Speaker 2' - these are placeholder labels that make published transcripts feel unfinished.

For panel discussions with 3+ speakers, use abbreviated first names consistently. Introduce each speaker once at the top of the transcript with their full name and title, then use the short form throughout: 'Rahul:', 'Priya:', 'Aditya:'.

In Hinglish interviews, speakers often have distinct mixing patterns. One speaker might use 70% Hindi with occasional English nouns, while another uses 50-50 mixing. These patterns become natural identifiers once you hear them. During the first listen-through, note each speaker's style to help with attribution when the AI output is ambiguous.

Watch for turn-taking cues in Hindi: 'haan toh' (yes so), 'achha' (okay/right), and 'dekhiye' (see/look) often signal a speaker change. The AI may not always detect these boundaries correctly.

Place the speaker label on its own line or at the start of their first sentence in each turn. Use bold or a colon to visually separate the label from the content. Be consistent - if you use 'Rahul:' in paragraph 1, do not switch to 'R:' or 'Rahul says:' later.

For timestamps, place them before the speaker label: '[02:34] Rahul: Main ye kehna chahta tha ki...' This format allows readers to jump to specific moments while knowing who is speaking.

Indian conversations frequently have overlapping speech - two people talking simultaneously is normal, not rude. In transcripts, represent this with brackets: '[overlapping] Priya: Haan exactly, main bhi - Rahul: - wahi point tha mera.' or place each speaker's overlapping content on separate lines with a note.

For published content where readability matters more than fidelity, simplify overlaps. Keep whichever speaker made the substantive point and note the other's agreement parenthetically: 'Rahul: Wahi point tha mera. (Priya agrees)'.

AI diarization (speaker separation) works well when speakers have distinct voices - different pitch, different pace, or male/female distinction. It struggles when two speakers have similar voices, when one speaker is much quieter, or when cross-talk is frequent.

Fix attribution errors during your proofreading pass. Look for sudden changes in speaking style within a single attributed block - if 'Rahul' suddenly starts using vocabulary patterns that match Priya, the label is probably wrong from that point.