Conversational speech → Automatic speech recognition (ASR)
Conversational speech for Automatic speech recognition (ASR)
Automatic speech recognition (ASR) needs acoustic variety, real conditions, accurate transcripts, and speaker/accent coverage. 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 Automatic speech recognition (ASR)
ASR accuracy is not one number. Systems that report low word error rates on read-speech benchmarks routinely degrade — often by a factor of two or more — on spontaneous conversation, because conversation contains what benchmarks lack: disfluencies, restarts, overlapping talk, fast turn-taking, code-switching, and far-field acoustics. Conversational speech is the training and evaluation material that closes that gap. It teaches the acoustic model real prosody and the language model real syntax — "I was gonna— wait, did you—" is valid input, not noise. It also exposes the failure modes that matter commercially: accented speech, phone codecs, background chatter. A team whose product transcribes meetings, calls, or clinics is shipping against conversational audio, so training on read speech alone means evaluating against a different distribution than production traffic.
What buyers typically spec
Industry-typical ranges — a brief can and should deviate where the task demands it.
| Typical volume | Hundreds to low thousands of hours for fine-tuning; 10–50 h held out for evaluation |
|---|---|
| Audio | 16 kHz+ mono per speaker; far-field room channel kept alongside close-talk where possible |
| Labels | Verbatim transcripts with disfluencies, timestamps, speaker turns, overlap and noise marks |
| Metadata | Accent/dialect, device, environment, speaker demographics |
| Formats | WAV/FLAC audio; JSON or CTM/RTTM-style alignment files |
A sample brief
The shape of a workable request — swap in your own numbers and conditions:
- Modality: unscripted two-party phone and VoIP conversations, 20–40 min each.
- Volume & coverage: 500 hours; en-US and en-IN accents, balanced by speaker sex and age band.
- Labels: verbatim transcripts with disfluencies, per-turn timestamps, overlap flags.
- Rights: signed licence with explicit AI-training grant; consent from all speakers on file.
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
How should WER be measured on disfluent speech?
Agree the text normalization first. Whether "um", restarts, and contractions count as errors changes WER by whole points, so the corpus should ship verbatim transcripts plus a stated normalization convention. Verbatim ground truth lets you score strict or normalized; cleaned-only transcripts lock you into one.
Is heavy overlap a problem in ASR training data?
It is a feature to control, not remove. Overlap is where production systems fail, so training sets should include it labelled, and evaluation sets should report it as a slice. What you want to avoid is unlabelled overlap silently corrupting alignments.
Does code-switching need special handling?
Yes — transcripts need language tags at the switch points, and the brief should state target language pairs. Untagged code-switched speech trains poorly and evaluates worse, because the scorer cannot tell a language switch from an error.
Other data for Automatic speech recognition (ASR)
More Conversational speech use cases
Need Conversational speech for Automatic speech recognition (ASR)?
Whether you're building a model or sitting on an archive, the first conversation is short and specific.
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