The field of speaker extraction and adaptation is moving towards more efficient and effective methods for isolating target speakers in complex audio environments. Recent developments have focused on improving the generalizability and discrimination of speaker embeddings, as well as enabling zero-shot adaptation and real-time processing. Notably, researchers are exploring innovative approaches such as meta-learning and multi-modal fusion to advance the state-of-the-art in speaker-dependent voice modeling and speech recognition. These advancements have significant implications for applications in speech-based health monitoring, dysarthric speech recognition, and real-time audio processing.

Noteworthy papers include: On-the-fly Routing for Zero-shot MoE Speaker Adaptation of Speech Foundation Models for Dysarthric Speech Recognition, which proposes a novel MoE-based speaker adaptation framework for foundation models. Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models, which reformulates speaker-dependent modeling as a meta-learning problem and explores several approaches for predicting time since sleep from speech.