Causal-anticausal consistency co-training recovers about 70% of the boundary-tightening effect possible with ideal tight labels in speaker diarization.
Efficient and generalizable speaker diarization via structured pruning of self-supervised models
4 Pith papers cite this work. Polarity classification is still indexing.
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LLM-based multi-talker ASR with dual-encoder, feature interleaving, length-aware speaker loss, and adaptive ASR threshold achieves 18% and 24% relative gains over baselines on AliMeeting and Aishell4.
Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.
Cross-lifespan evaluation shows adult-trained speech foundation models degrade on child and older-adult data, with joint multi-age training and targeted adaptation improving robustness especially using Whisper encoder.
citing papers explorer
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Tight Boundary Prediction in Speaker Diarization Using Causal-Anticausal Consistency
Causal-anticausal consistency co-training recovers about 70% of the boundary-tightening effect possible with ideal tight labels in speaker diarization.
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Balancing ASR and diarization in end-to-end LLMs for multi-talker speech recognition
LLM-based multi-talker ASR with dual-encoder, feature interleaving, length-aware speaker loss, and adaptive ASR threshold achieves 18% and 24% relative gains over baselines on AliMeeting and Aishell4.
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Audio-Mind: An Auditable Agentic Framework for Audio Understanding
Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.
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Exploring Speech Foundation Models for Speaker Diarization Across Lifespan
Cross-lifespan evaluation shows adult-trained speech foundation models degrade on child and older-adult data, with joint multi-age training and targeted adaptation improving robustness especially using Whisper encoder.