DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Supervised Speech Foundational Model
Abstract
Self-supervised speech models have achieved remarkable success on content-driven tasks, yet they remain limited in capturing speaker-discriminative features critical for verification, diarization, and profiling applications. We introduce DELULU, a speaker-aware self-supervised foundational model that addresses this limitation by integrating external supervision into the pseudo-label generation process. DELULU leverages frame-level embeddings from ReDimNet, a state-of-the-art speaker verification model, to guide the k-means clustering step during pre-training, introducing a strong speaker-discriminative inductive bias that aligns representation learning with speaker identity. The model is trained using a dual objective that combines masked prediction and denoising, further enhancing robustness and generalization. DELULU significantly outperforms prior self-supervised learning (SSL) models across a range of speaker-centric tasks, achieving up to 62% relative improvement in equal error rate (EER) for speaker verification and consistent gains on zero-shot profiling tasks such as gender, age, accent, and speaker counting. Our findings demonstrate that DELULU is a strong universal encoder for speaker-aware speech processing, enabling superior performance even without task-specific fine-tuning.
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 DELULU, a speaker-aware self-supervised foundational model that significantly enhances speaker-discriminative feature extraction in speech processing tasks. The innovative integration of external supervision and a dual training objective positions this work as a substantial advancement in the field of self-supervised learning for audio applications.
Methodology
The methodology presented in DELULU is innovative, particularly in its integration of external supervision into the pseudo-label generation process. By utilizing frame-level embeddings from ReDimNet, the authors effectively introduce a speaker-discriminative inductive bias that enhances representation learning. The dual objective of masked prediction and denoising is a thoughtful approach that likely contributes to the model's robustness and generalization capabilities. However, the paper could benefit from a more detailed explanation of the k-means clustering step and how it interacts with the overall training process.
Results & evaluation
The experimental setup is comprehensive, with a clear focus on speaker-centric tasks. The reported results, including a 62% relative improvement in EER for speaker verification, are impressive and demonstrate the effectiveness of DELULU. The inclusion of zero-shot profiling tasks adds depth to the evaluation, showcasing the model's versatility. However, the paper lacks a comparison with a broader range of existing models, which would strengthen the claims of superiority.
Reproducibility
The paper does not provide sufficient details on implementation, such as hyperparameters, training duration, or dataset specifics, which are crucial for reproducibility. While the results are promising, the absence of a code repository or supplementary materials limits the ability for other researchers to replicate the findings.
Limitations
The paper acknowledges limitations, particularly in the reliance on external supervision, which may not be available in all scenarios. Additionally, the model's performance on diverse datasets beyond the ones tested could be a concern, as generalizability is critical in real-world applications.
Broader impact
DELULU has significant potential applications in speaker verification, diarization, and profiling, which are increasingly relevant in various sectors, including security and customer service. The model's ability to operate effectively without task-specific fine-tuning is particularly noteworthy, as it could facilitate broader adoption in practical applications. The main contribution of this paper is the introduction of DELULU, a speaker-aware self-supervised foundational model that significantly enhances speaker-discriminative feature extraction in speech processing tasks. The innovative integration of external supervision and a dual training objective positions this work as a substantial advancement in the field of self-supervised learning for audio applications.
Author of this paper? Send a correction.