Speaker Change Point Detection With Confidence Scores

via “confidence-scoring-and-uncertainty-quantification”

automatic-speech-recognition model by undefined. 49,28,734 downloads.

Unique: Extracts token-level confidence scores directly from the model's softmax distribution during decoding, enabling fine-grained uncertainty quantification without additional inference passes. Scores are computed end-to-end within the transcription pipeline.

vs others: Faster than ensemble-based uncertainty methods (e.g., multiple model runs) because confidence is computed in a single pass; however, less reliable than Bayesian approaches or ensemble methods because single-model confidence scores are poorly calibrated and do not account for systematic model errors.

via “speaker-change-point-detection-with-confidence-scores”

automatic-speech-recognition model by undefined. 1,02,76,778 downloads.

Unique: Computes change point confidence by analyzing embedding similarity across frame boundaries and speaker assignment stability, rather than using simple threshold-based detection. Integrates with the diarization pipeline to provide confidence-weighted change points.

vs others: Provides confidence-scored change points compared to binary detection in simpler systems, enabling downstream filtering and ranking. More accurate than energy-based or spectral-based change point detection.

via “speaker-count-estimation-via-similarity-analysis”

automatic-speech-recognition model by undefined. 27,65,322 downloads.

Unique: Combines multiple statistical heuristics (gap statistic, silhouette analysis, knee-point detection) and uses ensemble voting to estimate speaker count, improving robustness vs. single-method approaches. Produces confidence scores based on agreement between heuristics.

vs others: More robust than fixed-k clustering; automatic speaker count detection vs. manual specification; ensemble approach reduces sensitivity to individual heuristic failures.

via “confidence-scored speech segmentation with temporal boundaries”

automatic-speech-recognition model by undefined. 30,94,665 downloads.

Unique: Converts frame-level neural predictions into segment-level output with learned confidence scoring rather than simple thresholding; confidence reflects model uncertainty and can be calibrated per domain through post-hoc scaling

vs others: More interpretable than raw frame predictions and enables quality filtering; more flexible than fixed-threshold segmentation by providing confidence-based filtering options

via “speaker-change-detection”