Prototype summary
Indriya developed a conversation-analysis prototype combining speech recognition with speaker diarization. The initial research focused on anonymous speaker attribution, speaker changes and overlapping speech in meetings, interviews and discussion-style recordings.
The work used a pyannote-based pipeline, investigated configuration changes and included targeted adaptation of speech segmentation. Evaluation separated speaker-assignment errors from transcription errors. The prototype produces speaker-attributed transcripts and supports review of difficult conversational segments.
Further work focuses on consistency across unfamiliar speakers, microphones and recording environments, together with operational hardening for pilots. Comparative findings are under internal review.
1. Anonymous attribution
Speaker diarization groups portions of a recording according to the speaker. In this prototype, attribution is anonymous: labels distinguish conversational speakers without claiming their real-world identities. A label attached to a transcript turn is not a biometric identity finding.
The research examines whether words and turns remain assigned consistently as people alternate, pause or overlap. A transcript can contain correct words and still attribute them to the wrong speaker. That changes how a reader interprets a conversation.
2. Segmentation and configuration
The pyannote toolkit supplies building blocks for speaker diarization, including speech activity, changes and overlap. Indriya used a pyannote-based pipeline in the prototype and investigated configuration changes and targeted speech-segmentation adaptation.
Segmentation concerns the portions of audio treated as speech and their boundaries. In a conversation-analysis pipeline, those choices interact with the turns a reviewer sees. The programme examined them as part of speaker-attributed transcription rather than treating diarization as a label added after all other processing.
A reproducible deployment will need to retain the selected pipeline, adapted checkpoint and configuration together. Upstream toolkit examples provide background; the prototype’s own configuration belongs with its evaluation record.
3. Changes and overlapping speech
Meetings, interviews and discussions can include quick speaker changes and simultaneous speech. The research focused on these segments because they complicate the relationship between an audio interval, its words and the speaker label.
The prototype supports review of difficult conversational segments. Returning to the recording lets a reviewer assess an uncertain attribution alongside the recognized words.
Unfamiliar voices and different microphones remain part of the ongoing validation work. Conditions in a controlled experiment do not automatically represent every meeting room, headset or remote-call recording.
4. Keep the error sources separate
Evaluation separated speaker-assignment errors from transcription errors. This avoids attributing a change in recognition quality to diarization, or interpreting a speaker-label improvement as better transcription.
The two outputs still need to be examined together. A correct speaker label does not repair a misheard number; correct words do not repair the wrong speaker label. A conversation-analysis result depends on their alignment as well as the quality of each component.
A reproducible comparison needs the recordings, speaker and overlap conditions, reference annotations, pipeline configuration and scoring method.
5. Preparation for pilots
Operational hardening and consistency across unfamiliar speakers, microphones and recording environments are ongoing. The broader programme’s pilot preparation covers reproducibility, installation, monitoring and performance under representative conditions.
AI-assisted workflows supported preparation, experimentation, evaluation scripts and error categorization. Humans governed research design, data acceptance and the interpretation of findings. The resulting assets include a prototype and evaluation procedures within the internal technical report.
Recognition models and speaker attribution
NVIDIA recognition models and audio-capable Nemotron and Gemma variants are relevant candidates for the recognition component of further comparisons. Apple AFM 3 provides a separate platform-access investigation. None should be treated as a replacement for anonymous diarization merely because it accepts audio.
When changing the recognition component, keep the diarization configuration and recordings fixed. Report speaker-assignment errors separately from word errors, and preserve uncertain or overlapping segments for review.
References and publication status
References checked on 14 October 2026.