Multimodal Target Speaker Extraction: Towards Unified Speaker Cues Across Modalities

2026-09-28

AI Summary

This study addresses the reliability limitations of traditional audio enrollment under similar timbres, emotional variations, and noise interference by systematically reviewing deep learning-based multimodal target speaker extraction (TSE) techniques. It is the first to delineate the synergistic mechanisms between five categories of cross-modal cues—including visual and spatial information—and corresponding model architectures. The work traces the evolutionary trajectory from discriminative models to foundation models while integrating cutting-edge approaches such as diffusion models, flow matching, and neural codecs. Furthermore, this project constructs a comprehensive technical landscape of multimodal TSE, identifies adaptive fusion and instruction-driven extraction as promising future directions, and highlights critical bottlenecks regarding data scarcity and privacy preservation alongside proposed mitigation strategies.

Abstract

Target Speaker Extraction (TSE) is pivotal in speech communication and human-computer interaction, enabling the isolation of a specific speaker's voice from complex acoustic environments, i.e., the cocktail party scenario. Although traditional TSE systems conditioned on enrollment speech have progressed substantially, enrollment speech as a cue has inherent limitations. Its reliability degrades when the target and interfering speakers have similar voice characteristics, when intra-speaker variability (e.g. changes in emotion or speaking style) creates a mismatch between the enrollment and target speech, or when the enrollment itself is contaminated by noise or competing speakers. This review surveys deep-learning-based TSE from the perspective of auxiliary target cues drawn from multiple modalities. We organize existing methods according to five types of information used to isolate the target speaker: audio enrollment, visual, spatial, textual/semantic, and neural cues. We also trace the evolution from discriminative estimators to variational, diffusion, flow, codec, and foundation-model-based systems and summarize representative datasets and evaluation metrics. We review the benefits and limitations of different cues and discuss challenges involving synchronization, missing or unreliable observations, data scarcity, privacy, computational cost, and real-time operation. Finally, we summarize future directions concerning adaptive cue fusion, instruction-driven extraction, realistic evaluation, and trustworthy deployment. By jointly reviewing cue design, model architecture, training objectives, datasets, and evaluation metrics, this article provides an overview of the current landscape and open problems in multimodal TSE.

Problem

• Target Speaker Extraction
• Multimodal Cues
• Cocktail Party Problem
• Speaker Enrollment
• Deep Learning

Innovation

• Target Speaker Extraction
• Multimodal Cues
• Diffusion Models
• Foundation Models
• Adaptive Cue Fusion