Papers for

automatic transcription services

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Speaker diarization errors split to clarify pause ambiguity effects

Diarization Error Decomposition Under Pause Annotation Ambiguity

Abstract: Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morphological closing, which has been used for pause-tolerant diarization evaluation, discards segment-level distinctions. Instead, we propose an exact, overlap-aware decomposition of standard DER into a pause-attributable component, consisting of errors compatible with pause filling, and a residual core component that can serve as a proxy for intrinsic diarization errors. The decomposition leaves DER unchanged, while the pause-attributable and core components vary monotonically with the pause threshold and eventually saturate. Experiments spanning synthetic transformations, annotation mismatch, cross-domain evaluation, and tight-boundary diarization show that the decomposition reveals error sources not apparent from standard DER.

Thu 10 SeptSound
The gist
Speaker diarization means figuring out who spoke when in an audio recording. Sometimes, pauses between speakers are marked differently, which can make error rates look worse or hide real mistakes. The authors found that a common way to handle pauses loses important details about segments. They created a new method that breaks down errors into those caused by pause ambiguities and the core diarization mistakes, helping to better understand what went wrong.
Open 2609.11007v1