EvoTSE: Evolving Enrollment for Target Speaker Extraction
Abstract
Target Speaker Extraction (TSE) aims to isolate a specific speaker's voice from a mixture, guided by a pre-recorded enrollment. While TSE bypasses the global permutation ambiguity of blind source separation, it remains vulnerable to speaker confusion, where models mistakenly extract the interfering speaker. Furthermore, conventional TSE relies on static inference pipeline, where performance is limited by the quality of the fixed enrollment. To overcome these limitations, we propose EvoTSE, an evolving TSE framework in which the enrollment is continuously updated through reliability-filtered retrieval over high-confidence historical estimates. This mechanism reduces speaker confusion and relaxes the quality requirements for pre-recorded enrollment without relying on additional annotated data. Experiments across multiple benchmarks demonstrate that EvoTSE achieves consistent improvements, especially when evaluated on out-of-domain (OOD) scenarios. Our code and checkpoints are 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 main contribution of this paper is the introduction of EvoTSE, a novel framework for Target Speaker Extraction that dynamically updates speaker enrollments to mitigate speaker confusion and improve performance in challenging audio environments. This work significantly advances the state of the art in TSE, particularly in handling out-of-domain scenarios, and provides a solid foundation for future research in audio processing and speaker identification.
Methodology
The proposed EvoTSE framework innovatively addresses the limitations of static enrollment in Target Speaker Extraction (TSE) by introducing a dynamic, evolving enrollment mechanism that utilizes historical context to adaptively update speaker cues. The methodology integrates a contextual retriever, backbone extractor, reliability classifier, and memory curator, which collectively enhance the robustness of speaker extraction in long-duration audio scenarios. The approach is well-structured and leverages existing concepts like Retrieval-Augmented Generation (RAG) while extending them into the audio domain, showcasing a thoughtful adaptation of techniques to solve a specific problem in TSE.
Results & evaluation
The experimental setup is comprehensive, utilizing multiple datasets including WSJ0-2mix, Libri2mix-clean, and a newly constructed Emotional Speech Database (ESD) to evaluate the model's performance across various conditions. The results demonstrate consistent improvements in extraction quality, particularly in out-of-domain scenarios, which is a significant contribution to the field. The use of multiple evaluation metrics, including SI-SDRi and NSR, provides a robust framework for assessing the model's effectiveness.
Reproducibility
The paper provides sufficient implementation details, including model configurations and training strategies, which enhance reproducibility. However, the absence of a clear mention of the specific venue or publication may hinder broader accessibility to the research community. The availability of code and checkpoints on GitHub is a positive aspect that supports reproducibility.
Limitations
One limitation is the reliance on the quality of historical estimates, which may introduce noise if the initial enrollment is poor. Additionally, while the framework shows promise in OOD scenarios, the paper does not extensively discuss the computational complexity or real-time applicability of the EvoTSE framework in practical applications.
Broader impact
The EvoTSE framework has significant implications for real-world applications such as voice assistants, automated transcription services, and any system requiring speaker identification in noisy environments. By improving the robustness of TSE, this work could enhance user experiences in various audio processing applications, particularly in dynamic and emotionally varied contexts. The main contribution of this paper is the introduction of EvoTSE, a novel framework for Target Speaker Extraction that dynamically updates speaker enrollments to mitigate speaker confusion and improve performance in challenging audio environments. This work significantly advances the state of the art in TSE, particularly in handling out-of-domain scenarios, and provides a solid foundation for future research in audio processing and speaker identification.
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