Multi-Speaker ASR

ASR: Automatic Speech Recognition

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13 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition
Sep 24, 2026Bo Su, Yueru Yan, Thai LePrivacy-Preserving Speech Processing Automatic Speech Recognition
Indiana University, Bloomington, USA
STAM-ASR: Speaker-Temporal Anchoring with Memory for Multi-Speaker ASR
Sep 24, 2026Victor Tolulope Olufemi, Syeda Faiza Ahmed Sara, Shammur Absar ChowdhuryMulti-Speaker ASR Audio-Language Models
Qatar Computing Research Institute (QCRI), Doha, Qatar
Pushing the Boundaries of Streaming Multi-Speaker ASR: A Systematic Study of Architectural Trade-offs
Sep 9, 2026Taejin Park, Ivan Medennikov, Kunal Dhawan +3Multi-Speaker ASR Streaming ASR
NVIDIA, Santa Clara, CA, United States
Zero-Shot Temporal Localisation of Audio Deepfakes in Multi-Speaker Conversations
Sep 9, 2026Soumyadeep RoyAudio Deepfake Detection Multi-Speaker ASR
Postgraduate Department of Data Science, St. Xavier’s College (Autonomous), Kolkata, India · Institute for Advancing Intelligence (IAI), TCG CREST, Kolkata
Soft Posterior Speaker Injection for Multi-Talker Speech Recognition
Sep 1, 2026Jian Zhu, Jun Sun, Jiang Yang +6Multi-Speaker ASR Automatic Speech Recognition
Zhejiang Lab, Hangzhou, China · Zhejiang International Studies University, Hangzhou, China · University of Science and Technology of China, Hefei, China
DiaScriber: A Speech LLM for Joint Diarization and Transcription in Multi-Speaker Scenarios
Aug 24, 2026Bingshen Mu, Xian Shi, Xiong Wang +6Multi-Speaker ASR Automatic Speech Recognition
Independent Researcher
Mitigating Speaker Leakage in Cascaded Multi-talker ASR with Diarization-based Transcript Correction
Aug 23, 2026Hermann Yepdjio Nkouanga, Minwei Luo, Maggie Wigness +1Multi-Speaker ASR Automatic Speech Recognition
Portland State University, USA · US Army Research Laboratory, USA
On the Role of Conversational Timing in Synthetic Training Data for ASR
Jul 9, 2026Máté Gedeon, Péter MihajlikMulti-Speaker ASR Automatic Speech Recognition
Dept. of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Hungary · Speechtex Ltd.
H-SAGE: Holistic Speaker-Aware Guided Experts for MoE-based Multi-Talker ASR
Jul 2, 2026Yujie Guo, Jiaming Zhou, Yuhang Jia +2Multi-Speaker ASR Automatic Speech Recognition
TMCC, College of Computer Science, Nankai University, Tianjin, China
Balancing ASR and diarization in end-to-end LLMs for multi-talker speech recognition
Jun 11, 2026Naijun Zheng, Yuke Lin, Sanli Tian +4Speaker-Attributed ASR Multi-Speaker ASR
Huawei Technologies, China
Beyond Acoustic Prefixes: Persistent Access to Serialized Acoustic Memory for LLM-Based Multi-Talker Speech Recognition
Mar 28, 2026Hao Shi, Yuan Gao, Xugang Lu +1Multi-Speaker ASR Memory-Augmented Language Models
Graduate School of Informatics, Kyoto University, Kyoto, Japan · National Institute of Information and Communications Technology, Japan
Distilling LLM Semantic Priors into Encoder-Only Multi-Talker ASR with Talker-Count Routing
Mar 11, 2026Hao Shi, Yusuke Fujita, Roman Koshkin +4Multi-Speaker ASR Connectionist Temporal Classification
SB Intuitions, Tokyo, Japan
TagSpeech: End-to-End Multi-Speaker ASR and Diarization with Fine-Grained Temporal Grounding
Jan 11, 2026Mingyue Huo, Yiwen Shao, Yuheng ZhangMulti-Speaker ASR Audio Understanding
University of Illinois Urbana-Champaign · Johns Hopkins University

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Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition

Bo Su, Yueru Yan, Thai Le

Indiana University, Bloomington, USA

Privacy-Preserving Speech Processing Automatic Speech Recognition Multi-Speaker ASR Speech Language Models

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We introduce target-speaker unlearning ASR (TSU-ASR) task in a fully end-to-end framework for multi-speaker ASR and diarization. Given a multi-speaker utterance and a set of opt-out speakers who do not wish to have their speech transcribed, the task requires an ASR system to transcribe all speakers except the opt-out ones, while still indicating when those speakers are active. As a first step towards tackling this task, we introduce a novel, light-weight Enrollment-Conditioned Gating (ECG) module attachable to a frozen dual-stream speech LLM that enables ASR for new opt-out speakers dynamically during inference, even those who were not seen during initial ECG training phase. Our experiments on both AMI (English) and AliMeeting (Mandarin) datasets show that speech transcription accuracy for corresponding opt-out words or characters falls from 72.3% to 48.2% and from 73.6% to 27.3%, respectively, while retained speakers' transcription error rates maintain more or less the same. Our approach provides a practical solution for modern video conferencing platforms, allowing speakers to dynamically opt-out from automated AI transcriptions without forcefully leaving the meeting sessions, enabling a privacy-preserving interface for potentially millions of online meetings daily.