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American English Full-Duplex Two-Speaker Conversational Dataset
Dataset summary
Natural, unscripted, two-speaker English conversations recorded by fluent English speakers based in the United States and Canada. Each session is a ~15-minute spontaneous discussion between a matched pair of speakers on everyday topics — personal experiences, hobbies, workplace challenges, or opinions that have changed over time.
The recordings are designed to support the development of next-generation AI systems — helping them better understand natural speech patterns, conversational flow, turn-taking, and real-world human interaction. Each speaker is captured on an independent, isolated audio track, enabling per-speaker analysis, diarization, full-duplex modeling, ASR, and TTS.
Dataset statistics
| Conversations | 954 |
| Per-speaker tracks (rows) | 1,908 |
| Conversation audio | ~230 hours |
| Isolated per-speaker audio (both tracks) | ~455 hours |
| Avg / median conversation length | 14.5 / 15.0 min |
| Length range | 0.4 – 15.3 min |
Each conversation contributes two simultaneous isolated speaker tracks, so the ~230 hours of conversation yields ~455 hours of clean single-speaker audio.
How it was collected
Participants were fluent U.S./Canada-based English speakers recording remotely:
- Conversational recording — two matched partners hold a ~15-minute recorded conversation. Sessions are unscripted; partners choose a topic together.
- Natural interaction — speakers listen actively, respond thoughtfully, and build on each other's ideas in a clear, natural way.
- Topic selection — real-life topics (personal experiences, hobbies, workplace challenges, evolving opinions). The chosen prompt is stored per row.
- Audio quality — speakers followed guidelines to keep clear, consistent audio throughout each session.
Each conversation produces two simultaneous per-speaker tracks (speaker_a and
speaker_b), recorded full-duplex.
Dataset structure
One row per speaker track, stacked and ordered by room_name so a conversation's two speakers sit adjacent. Each row is one isolated voice with its own metadata; join on room_name to reconstruct the conversation.
Audio is the original Opus capture in a .opus container.
Fields (per row = one speaker's track)
| field | description |
|---|---|
file_name |
this speaker's isolated audio track |
room_name |
conversation/session key — shared by both speakers of a conversation |
conversation_id |
conversation identifier |
role |
SPEAKER_A or SPEAKER_B |
speaker_id |
stable speaker identifier |
duration_seconds |
track duration |
language |
spoken language of the session (en-US) |
prompt |
the conversation topic the pair discussed |
gender |
self-reported |
city, country |
self-reported location |
ethnicity |
self-reported |
fluent_languages |
languages the speaker is fluent in |
Rows are ordered by room_name so a conversation's two speakers appear
adjacent. Join on room_name to reconstruct a full conversation.
Privacy & consent
Speaker names and emails are removed. Demographic fields are self-reported. Recordings were collected from consenting, compensated participants for AI research. This is a gated dataset — access requires agreeing to research-only use and no re-identification.
Audio note
Tracks are Opus in a opus container. Decode with datasets>=4.0
(torchcodec/FFmpeg). For older stacks, losslessly rewrap to .opus/.ogg
(ffmpeg -i in.opus -c:a copy out.opus).
License
Released under CC-BY-NC-4.0 (research / non-commercial). Contact OcularAI for other licensing.
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