Pushing the limits of self-supervised speaker verification using regularized distillation framework (2211.04168v4)

Abstract: Training robust speaker verification systems without speaker labels has long been a challenging task. Previous studies observed a large performance gap between self-supervised and fully supervised methods. In this paper, we apply a non-contrastive self-supervised learning framework called DIstillation with NO labels (DINO) and propose two regularization terms applied to embeddings in DINO. One regularization term guarantees the diversity of the embeddings, while the other regularization term decorrelates the variables of each embedding. The effectiveness of various data augmentation techniques are explored, on both time and frequency domain. A range of experiments conducted on the VoxCeleb datasets demonstrate the superiority of the regularized DINO framework in speaker verification. Our method achieves the state-of-the-art speaker verification performance under a single-stage self-supervised setting on VoxCeleb. Code has been made publicly available at https://github.com/alibaba-damo-academy/3D-Speaker.

• The paper introduces RDINO, a novel self-supervised framework with diversity and redundancy regularization to prevent model collapse.
• It employs multi-stage audio augmentations such as WavAugment, SpecAugment, and acoustic feature shuffling to enhance training variability.
• Evaluations on VoxCeleb datasets demonstrate that RDINO achieves an EER of 3.29% and a MinDCF of 0.247, outperforming conventional SSL methods.

Pushing the Limits of Self-Supervised Speaker Verification Using Regularized Distillation Framework

This paper addresses the challenges inherent in speaker verification without speaker labels, a domain where self-supervised learning (SSL) frameworks often face significant performance gaps compared to supervised methods. The authors implement and enhance a non-contrastive self-supervised learning framework, specifically "DIstillation with NO labels" (DINO), by introducing two novel regularization terms tailored for speaker verification tasks.

Key Innovations and Methodology

The paper presents an extension to DINO by incorporating two critical regularization components. These aim to mitigate the prevalent issue of model collapse—a scenario where the model outputs become trivial and non-informative. The proposed framework, Regularized DINO (RDINO), makes use of:

1. Diversity Regularization: This term ensures diversity within a batch by forcing variance among embeddings, thereby avoiding trivial solutions where embeddings might converge to a singular point.
2. Redundancy Elimination Regularization: This encourages decorrelation among embedding dimensions, aiming to eliminate redundant information and enhance distinguishability.

A multi-stage augmentation strategy is also explored, including WavAugment, SpecAugment, and Acoustic feature shuffling, to introduce variability within the training data.

Results and Numerical Outcomes

Significant empirical validation is provided through experiments conducted on the VoxCeleb datasets, specifically on the test set of VoxCeleb1, using the development set of VoxCeleb2 for training. Importantly, VoxCeleb utilized contains over one million utterances from 5,994 speakers. Notably, RDINO sets a new benchmark by achieving an EER of 3.29% and a MinDCF of 0.247, outperforming existing SSL frameworks. The performance gains emphasize the efficacy of the proposed regularization terms in managing model collapse and enhancing the robustness of the method without necessitating pseudo-labels or large negative sample batches.

Discussion and Implications

The proposed single-stage, non-contrastive framework circumvents issues faced by typical self-supervised speaker verification systems that rely on two-stage approaches, often demanding accurate estimation of speaker numbers. By eliminating the need for negative samples and reducing reliance on large batch sizes, RDINO presents a computationally efficient alternative.

Additionally, the findings indicate how certain augmentation strategies—like WavAugment—contribute critically to model training success, while others, such as SpecAugment, introduce complexities not always aligned with training goals.

Future Directions

Looking forward, exploring more diverse audio datasets and environmental variances could further cement the robustness of self-supervised approaches in speaker verification. Additionally, integrating this framework into mixed-modality analysis, or extending it towards more nuanced speech representation tasks, remains promising. Understanding how these embeddings interact with dynamic, cross-cultural datasets could provide deeper insights into their practical applicability.

The research introduces a methodologically sound and computationally efficient approach to self-supervised speaker verification, taking a step towards reducing dependency on extensive labeled datasets and offering a scalable alternative for broad applications.