Fiund / Speech & conversation data
Speech data for AI
A specification that connects the source to the model task.
A clear review process
What to establish for your project.
Start with the objective
Describe the model, learning or evaluation task and the conditions it must handle. Recognition, synthesis and dialogue projects can require different source recordings and preparation.
Define audio and annotation
Set recording level, channels, encoding, sample rate, transcripts, timestamps and speaker-label requirements. Mark which fields are essential and which are preferences.
Review coverage and provenance
Assess source-session relationships, repeat speakers, language groups and editorial exclusions. Ask for the available evidence needed to connect proposed files to their origin and intended use.
Build a practical next step
Send a short project brief to Fiund. A first conversation can identify the missing requirements, review a suitable sample approach and establish the scope for a project-specific proposal.
Founder, Fiund
Your project contact
For research, engineering & procurement teams
A direct conversation.
A well-defined next step.
Speak directly with Jaeden Schafer about the recordings, documentation and preparation your speech-data project needs. Start with the model task and agree on a clear source-review and commercial scope.
Continue your buyer research
Accented-speech evaluation datasets — Accented-speech evaluation data: supported coverage labels, benchmark limits, channel confounders, independent references and sourcing budgets.
Code-switching speech datasets for ASR evaluation — Code-switching ASR benchmarks: natural versus prompted speech, mixed-script references, scoring rules, source rights and pilot acceptance.