arXiv AI
Jun 18

Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness

arXiv:2505. 21954v2 Announce Type: replace-cross Abstract: We present UniTalk, a novel dataset emphasizing challenging scenarios to enhance model generalization for the task of active speaker detection (ASD).

By Le Thien Phuc Nguyen, Zhuoran Yu, Khoa Quang Nhat Cao, Yuwei Guo, Tu Ho Manh Pham, Tuan Tai Nguyen, Toan Ngo Duc Vo, Lucas Poon, Tuan Khai Nguyen, Soochahn Lee, Yong Jae Lee
arXiv Computation and Language
Sep 28

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

The paper introduces a new target‑speaker unlearning task for automatic speech recognition (TSU‑ASR) that allows certain speakers to opt out of transcription while still indicating their presence. A lightweight Enrollment‑Conditioned Gating (ECG) module is added to a frozen dual‑stream speech LLM, enabling dynamic unlearning of new opt‑out speakers during inference. Experiments on AMI and AliMeeting datasets show significant drops in transcription accuracy for opt‑out speakers while preserving performance for retained speakers.

By Bo Su, Yueru Yan, Thai Le
arXiv Computation and Language
Oct 21

How to get the best and fairest performance out of your ASR system using activation steering

The paper explores activation steering as a method to enhance automatic speech recognition (ASR) performance and demographic fairness. It compares two techniques—paired activation subtraction and supervised optimization—finding that supervised optimization effectively improves overall ASR accuracy and fairness across gender, age, and dialect. The study also demonstrates that steering vectors trained on acoustically similar speakers can yield competitive or superior results, enabling few‑shot or zero‑shot adaptation without requiring demographic labels.

By Felix Herron, Alex Colagrande, Solange Rossato, Fran\c{c}ois Portet, Alexandre Allauzen, Chuyuan Li