SpeakerRPL v2: Robust Open-set Speaker Identification through Enhanced Few-shot Foundation Tuning and Model Fusion
Abstract
This paper proposes an improved approach for open-set speaker identification based on pretrained speaker foundation models. Building upon the previous Speaker Reciprocal Points Learning framework (V1), we first introduce an enhanced open-set learning objective by integrating reciprocal points learning with logit normalization (LogitNorm) and incorporating adaptive anchor learning to better constrain target speaker representations and improve robustness. Second, we propose a model fusion strategy to stabilize and enhance the few-shot tuning process, effectively reducing result randomness and improving generalization. Furthermore, we introduce a model selection method to ensure optimal performance in model fusion. Experimental evaluations on the VoxCeleb, ESD and 3D-Speaker datasets demonstrate the effectiveness and robustness of the proposed method under diverse conditions. On a newly proposed Vox1-O-like test set, our method reduces the EER from 1.28% to 0.09%, achieving a relative reduction of approximately 93%.
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
This paper presents a robust approach to open-set speaker identification that enhances the existing framework through innovative methodologies and comprehensive experimental validation. The contributions are significant, addressing key challenges in the field and demonstrating potential for real-world applications.
Methodology
The paper introduces a sophisticated methodology for open-set speaker identification by enhancing the Speaker Reciprocal Points Learning framework. The integration of logit normalization and adaptive anchor learning is a significant advancement that improves the robustness of speaker embeddings. The proposed model fusion strategy and model selection policy are innovative approaches that address the inherent randomness in few-shot learning, which is a notable contribution to the field.
Results & evaluation
The experimental evaluation is thorough, utilizing multiple datasets (VoxCeleb, ESD, 3D-Speaker) to validate the proposed method. The results demonstrate substantial improvements in performance metrics such as EER and minDCF, particularly on the Vox1-O-like test set. The comparative analysis with baseline methods and ablation studies effectively highlights the advantages of the proposed enhancements.
Reproducibility
The paper provides sufficient details regarding the implementation and datasets, with a publicly available repository that enhances reproducibility. However, the absence of specific hyperparameter settings and training configurations may pose challenges for complete replication of results.
Limitations
One limitation is the reliance on synthetic data for augmenting unknown speakers, which may not fully capture the variability present in real-world scenarios. Additionally, while the model selection policy is effective, it may require further empirical validation across diverse datasets to ensure its generalizability.
Broader impact
The advancements presented in this paper have significant implications for applications in interactive voice systems, security, and human-computer interaction, where accurate speaker identification is critical. The methodology could also be extended to related tasks such as speaker diarization and emotion recognition, broadening its impact in the field of audio processing. This paper presents a robust approach to open-set speaker identification that enhances the existing framework through innovative methodologies and comprehensive experimental validation. The contributions are significant, addressing key challenges in the field and demonstrating potential for real-world applications.
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