most citedPruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring

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eess.AS2026

CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR

Muhammad Shakeel, Yosuke Fukumoto, Chikara Maeda +2

We present CALM, a joint Contextual Acoustic-Linguistic Modeling framework for multi-speaker automatic speech recognition (ASR). In personalized AI scenarios, the joint availabilit…

eess.AS2026★ 1 cited

Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder

Muhammad Shakeel, Yui Sudo, Yifan Peng +2

This paper presents a unified multi-speaker encoder (UME), a novel architecture that jointly learns representations for speaker diarization (SD), speech separation (SS), and multi-…