Multimodal cues improve target voice extraction in noisy settings
Multimodal Target Speaker Extraction: Towards Unified Speaker Cues Across Modalities
SoundMultimedia
Summary
Extracting a single person's voice from a noisy room with many people talking is hard, especially if voices sound alike or the example voice recording is unclear. The authors review research that uses different kinds of information, like visual cues, location, text, and brain signals, to better isolate a speaker’s voice. They explain how different approaches and technologies have developed over time and highlight the challenges when some cues are missing or unreliable. Their work helps show what methods work best and where more progress is needed for practical, trustworthy voice separation.
What this means in practice
- •For speech interface developers: Design voice assistants that reliably isolate user speech even with background noise using audio, visual, and other target cues.
- •For hearing aid engineers: Implement multimodal speaker extraction techniques to improve hearing aids’ ability to focus on a single speaker in noisy places.
A survey. It maps existing work.
Authors
Xinyuan Qian, Yanghao Zhou, Ziyang Jiang, Yu Chen, Xinjia Zhu, Xueyan Chen, Qiquan Zhang, Zexu Pan, Jiaying Wang, Xianghu Yue, Jiadong Wang, Björn Schuller, Haizhou Li
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.