Overlapped Speech Detection And Localization

via “overlapped-speech-detection-and-localization”

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

Unique: Detects overlap by analyzing speaker embedding consistency and acoustic divergence rather than relying on energy-based heuristics. The model learns to recognize acoustic signatures of simultaneous speech through supervised training on datasets with annotated overlaps.

vs others: Achieves 85-90% F1-score on overlap detection compared to 70-75% for energy-based or spectral-based overlap detection methods, with better generalization across acoustic conditions.

via “multi-speaker-overlap-detection-and-labeling”

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

Unique: Uses multi-task learning to jointly predict speaker embeddings and overlap probability, enabling the model to learn overlap-specific acoustic patterns (e.g., spectral masking, pitch differences) rather than treating overlap as a binary classification problem. Overlap labels are explicit outputs, not derived post-hoc.

vs others: More accurate than post-hoc overlap detection based on embedding similarity; explicit overlap labels enable downstream systems to handle overlapped speech differently; open-source vs proprietary overlap detection.