Bongjun Kim | Searcharxiv

arXiv2025

ASR-Synchronized Speaker-Role Diarization

Speaker-role diarization (RD), such as doctor vs. patient or lawyer vs. client, is practically often more useful than conventional speaker diarization (SD), which assigns only generic labels (speaker-1, speaker-2). The state-of-the-art end-to-end ASR+RD approach uses a single transducer that serializes word and role predictions (role at the end of a speaker's turn), but at the cost of degraded ASR performance. To address this, we adapt a recent joint ASR+SD framework to ASR+RD by freezing the ASR transducer and training an auxiliary RD transducer in parallel to assign a role to each ASR-predicted word. For this, we first show that SD and RD are fundamentally different tasks, exhibiting different dependencies on acoustic and linguistic information. Motivated by this, we propose (1) task-specific predictor networks and (2) using higher-layer ASR encoder features as input to the RD encoder. Additionally, we replace the blank-shared RNNT loss by cross-entropy loss along the 1-best forced-alignment path to further improve performance while reducing computational and memory requirements during RD training. Experiments on a public and a private dataset of doctor-patient conversations demonstrate that our method outperforms the best baseline with relative reductions of 6.2% and 4.5% in role-based word diarization error rate (R-WDER), respectively

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arXiv2019

Self-supervised Attention Model for Weakly Labeled Audio Event Classification

We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts of a weakly labeled audio clip in a more effective manner compared to prior attention models. We also propose a highly effective strongly supervised attention model when strong labels are available. This model also serves as an upper bound for the self-supervised model. The performances of the model with self-supervised attention training are comparable to the strongly supervised one which is trained using strong labels. We show that our self-supervised attention method is especially beneficial for short audio events. We achieve 8.8% and 17.6% relative mean average precision improvements over the current state-of-the-art systems for SL-DCASE-17 and balanced AudioSet.

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