Voice agents need training signal for turn-taking, overlap, interruption, and expressive delivery. This guide explains the labels and review fields that make speech data conversational.
By Datoric · Published July 21, 2026 · Last reviewed July 21, 2026
Read-speech corpora emphasize clear, isolated utterances. Conversational systems also need examples of speakers overlapping, interrupting, back-channeling, laughing, and taking turns on subtle timing cues. Those events should be represented explicitly when barge-in, turn-taking, or expressive delivery matters to the product.
Datoric's audio-video conversational program captures interruption, overlap, laughter, emotion, gesture, and turn timing alongside 48kHz audio and 1080p+ video. These documented events and cues give conversational and expressive models structured signal that scripted corpora leave out.
Annotated conversational events
Turn-taking is a timing problem: when does a speaker yield, and when is an incoming utterance an interruption rather than a back-channel? Explicit overlap and interruption labels, tied to diarization and timestamps, let a voice agent learn to detect barge-in and manage turns instead of talking over the user or freezing.
Expressive synthesis needs examples of laughter, emotional variation, and natural conversational delivery. Emotion labels and conversational-event annotations provide supervision for delivery as well as words. VoicePro-Bench reports that emotion classification remained below a usable routing signal in its evaluated audio-native models, supporting continued training and evaluation work in this area.
What to look for
Evidence and procurement
Frequently asked
Tell us the conversational dynamics and expressive range your model needs, and we will scope an audio-video program that captures them.
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