Conversational speech → Emotion & prosody
Conversational speech for Emotion & prosody
Emotion & prosody needs labelled emotional speech across speakers and contexts. Here is why Conversational speech is the right raw material for it, what buyers typically spec, and how the rights are handled.
Why Conversational speech for Emotion & prosody
Emotion models trained on acted corpora learn theatre, not feeling. Actors produce exaggerated, prototypical renditions — anger loud, sadness slow — while real affect in conversation is mixed, suppressed, and fleeting: frustration surfacing as a sigh inside an otherwise polite turn. Conversational speech is where genuine affect occurs, because emotion is relational; it happens between people. That realism comes with a labelling cost: annotator agreement on natural emotion is famously imperfect, so serious corpora report inter-annotator agreement, use multiple raters per clip, and often provide dimensional labels (valence/arousal) alongside categories, since dimensions degrade more gracefully than forced categories. Context also matters — a turn heard in isolation reads differently than in sequence — so labels should state whether raters heard surrounding turns.
What buyers typically spec
Industry-typical ranges — a brief can and should deviate where the task demands it.
| Typical volume | Tens of hours, densely labelled — label quality dominates raw volume here |
|---|---|
| Labels | Categorical emotion plus valence/arousal dimensions, per turn or per segment, multiple raters |
| Quality reporting | Inter-annotator agreement, rater count per clip, context given to raters |
| Coverage | Speakers, relationships, and contexts varied; both restrained and expressive affect |
| Formats | WAV/FLAC; JSON labels with per-rater values retained |
A sample brief
The shape of a workable request — swap in your own numbers and conditions:
- Modality: natural two-party conversations with genuine affect (support calls, debates, reunions).
- Volume: 40 hours, segmented to turns; 3+ raters per segment.
- Labels: valence/arousal plus categorical tags; agreement statistics delivered with the data.
- Rights: all-speaker consent naming AI training; sensitive-content handling agreed in the brief.
fiund's sourcing angle
Most speech corpora are scripted and read aloud. Genuine conversation — interruptions, crosstalk, accents, overlapping talk — is what speech and audio models are short on, and it is the supply fiund has the warmest path to. We source to a brief and clear the rights before anything moves, so what you receive is both useful and defensible in diligence.
Rights posture
Signed licence, explicit training rights, separate voice/likeness consent, nothing scraped. See the rights & provenance guides.
Frequently asked questions
What is wrong with acted emotional speech?
It is systematically exaggerated and prototypical, so models trained on it overfit to theatrical cues and miss the suppressed, blended affect that dominates real conversation. Acted data can help as augmentation; as the core corpus it optimizes for the wrong distribution.
What annotator agreement should I expect on natural emotion?
Modest — genuine affect is ambiguous, and honest corpora publish agreement statistics rather than hiding them. Prefer per-rater labels over collapsed majority votes: they let you train with soft targets and measure ambiguity instead of pretending it away.
Categorical or dimensional labels?
Both if possible. Categories (angry, happy) are easy to act on but force edge cases into boxes; valence/arousal dimensions capture mixed states and degrade gracefully. Dimensional labels with categorical tags on top is the most reusable scheme.
Other data for Emotion & prosody
More Conversational speech use cases
Need Conversational speech for Emotion & prosody?
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