A two-person interview is not the same technical problem as a six-person panel. SpeechKit documents a bounded speaker-labeling feature, and production workflows need to respect that limit.
What is actually promised
Yandex documents speaker labeling for recognition API v3 in FULL_DATA mode for mono audio, with recognition results limited to no more than two speakers. That is narrower than generic claims that a cloud recognizer always handles arbitrary meetings. The labels mark voice turns; they do not establish legal identity, consent or accuracy of names. A product editor should not rewrite a technical speaker index as “the CEO said” without independent knowledge of who was speaking.
Source recording choices matter
A mono conversation is not the same thing as channel-separated telephone audio. If callers arrive on separate channels, preserve a channel-based original even if the selected speaker-labeling mode later requires mono. Mixing speakers early can destroy information needed for audit. For an interview, keep room tone, pauses and overlap in the source file so reviewers can resolve ambiguous turns. Removing overlap with a heavy denoiser may make timing superficially cleaner while losing the very evidence a diarization model needs.
Integrate text and turn structure defensively
Treat provisional recognition segments as mutable. In live captions, do not permanently attribute a word the moment an unstable partial hypothesis appears. Persist audio time, text revision and speaker-label event separately, then reconcile after the final result. Apply sentence punctuation after the transcript has settled. Human editors should listen at transitions and interruptions, especially when both speakers are similar-sounding or one person speaks away from the microphone.
A realistic purchasing decision
For a two-speaker internal call, a service with a clear integration contract can be sufficient. For podcasts with guests entering mid-session, overlap-heavy roundtables or accessibility subtitles, compare a specialized diarization pipeline and human review. Declare the measurement you need: speaker-change F1, diarization error rate, word error rate, and time alignment are not equivalent. None is reported here as a tested result.
Privacy and retention questions
Speaker turn metadata can create sensitive inferences even when no biometric identity is intentionally computed. Limit who can review raw recordings, define retention separately for audio and transcript, and review any region-specific data-flow obligations. The 2026 SDK announcement documents integration paths, not a guarantee that speaker labeling supports every account or that all workloads meet local data-residency requirements.
Evidence, availability and commercial-rights boundary
Research cutoff: 25 October 2026. Provider documentation is evidence of its published interface or policy, not independent product testing. No performance score, hands-on trial, customer consent, territory-wide entitlement or legal clearance is claimed. Confirm the exact version, selected region, voice rights, data flow, account limits and current service terms before deployment. Unknown model release dates, account prices and plan limits are deliberately left unasserted; monetary claims belong to the separately dated AI10 pricing ledger. Where documentation is undated or dynamic, the public page is not treated as an archival snapshot of every earlier tier.