WeSep: A Modular and Cue-Composable Framework for Target Speaker Extraction
Abstract
The study of Target Speaker Extraction (TSE) aims to isolate a desired speaker from overlapping speech mixture given auxiliary cues. Existing systems are typically designed for specific cue types, limiting flexibility when cue availability varies across scenarios. We present WeSep, a unified framework that reformulates TSE as a heterogeneous cue-conditioned learning problem. In WeSep, cue modules and separator backbones are decoupled through standardized interfaces, enabling configurable cue injection and flexible integration of diverse modalities. The design enables systematic study of cue structure, intra- and cross-modal interaction, and dynamic cue availability within a shared optimization framework, facilitating adaptation to real-world conditions. Experiments across enrollment, spatial, visual, and textual cues reveal modality-dependent characteristics and demonstrate stable optimization under heterogeneous cue availability. The toolkit will be publicly available.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents WeSep, a modular framework for Target Speaker Extraction that standardizes cue injection and enables systematic study of heterogeneous cue availability. While it lacks algorithmic novelty, its contribution to software engineering and experimental standardization in the audio processing community is valuable and practically significant.
Methodology
The paper proposes WeSep, a modular framework for Target Speaker Extraction (TSE) that decouples cue encoders from separator backbones. The core methodological contribution is architectural rather than algorithmic; it introduces a standardized interface for injecting diverse cues (enrollment, spatial, visual, textual) into a shared optimization framework. This allows for composable multi-cue integration and handles heterogeneous cue availability (missing cues) via zero-padding. While the decoupling strategy is sound and addresses a real pain point in TSE (fragmented codebases for different modalities), the novelty is limited. The concept of modular TSE is not new (e.g., ClearerVoice, various multi-modal baselines), and the paper explicitly states it does not propose a new separation architecture. The "innovation" lies in the software engineering and experimental design rather than novel deep learning mechanisms.
Results & evaluation
The evaluation is comprehensive in terms of breadth, covering four distinct cue modalities and their combinations on standard datasets (Libri2Mix, VoxCeleb2-mix, etc.). The experiments successfully demonstrate that the framework supports stable training with missing cues and that combining cues (e.g., speaker + spatial) yields performance gains. However, the results are largely confirmatory. The paper shows that existing components work well when plugged into this framework, rather than demonstrating that WeSep introduces a new state-of-the-art method. The performance metrics (SI-SDRi) are standard, and the improvements are attributed to the flexibility of composition rather than a breakthrough in extraction capability. The causal modeling experiment is a practical addition but shows expected degradation in performance for low latency.
Reproducibility
The paper provides a GitHub URL for the toolkit, which is a significant positive for reproducibility. The experimental settings are described in reasonable detail, including backbone configurations (BSRNN, NBC2) and training hyperparameters (Adam, 150 epochs). The use of standard datasets enhances the ability of others to replicate the results. The modular design, if implemented cleanly as claimed, should facilitate reproduction by other researchers.
Limitations
The primary limitation is the lack of algorithmic novelty. The paper admits to not proposing a new separator. The performance gains are marginal and stem from better engineering and composition rather than fundamental improvements in the extraction task. The reliance on zero-padding for missing cues is a simple heuristic that may not be optimal compared to learned masking or attention mechanisms, although it is effective for stability. The paper does not provide a detailed ablation study on the *optimal* fusion strategies beyond what is standard, limiting the theoretical contribution.
Broader impact
This work has positive broader impact by promoting modularity and standardization in the TSE field. By providing an open-source toolkit, it lowers the barrier to entry for researchers working on multi-modal TSE and encourages systematic comparison of cue types. It facilitates research into robust TSE systems that can handle dynamic cue availability, which is crucial for real-world deployment. The paper presents WeSep, a modular framework for Target Speaker Extraction that standardizes cue injection and enables systematic study of heterogeneous cue availability. While it lacks algorithmic novelty, its contribution to software engineering and experimental standardization in the audio processing community is valuable and practically significant.
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