Spatially-Guided Speaker Extraction Based on Relative Position Cues

Regional target speaker extraction (RSE) aims to extract speech originating from a specified angular region, relaxing the requirement for precise target speaker location when performing location based speech extraction. However, existing RSE methods typically assume a fixed microphone array configuration and microphone number, limiting their applicability to unseen microphone geometries and varying numbers of microphones. In this work, we propose a geometry-aware RSE framework capable of extract with varying microphone array geometry and microphone numbers. Specifically, we introduce an attention-based multichannel front-end to encode inter-microphone phase information, together with a spatial audio reconstruction-based auxiliary objective to improve the front-end spatial representations quality. Experimental results demonstrate that the proposed framework improves SI-SDRi by 5.1 dB over geometry-aware baselines across microphone arrays with four-microphones, while maintaining a performance gap of less than 0.6 dB compared with a tetrahedral geometry-specific baseline when evaluated on the tetrahedral microphone array.

Target speaker extraction (TSE) aims to recover a target speaker's speech from a mixture using a reference utterance as a cue. Most TSE systems adopt conditional auto-encoder architectures with one-step inference. Inspired by test-time scaling, we propose a training-free multi-step inference method that enables iterative refinement with a frozen pretrained model. At each step, new candidates are generated by interpolating the original mixture and the previous estimate, and the best candidate is selected for further refinement until convergence. Experiments show that, when ground-truth target speech is available, optimizing an intrusive metric (SI-SDRi) yields consistent gains across multiple evaluation metrics. Without ground truth, optimizing non-intrusive metrics (UTMOS or SpkSim) improves the corresponding metric but may hurt others. We therefore introduce joint metric optimization to balance these objectives, enabling controllable extraction preferences for practical deployment.

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.