Neural Scoring, Not Embedding: A Novel Framework for Robust Speaker Verification

Current mainstream speaker verification systems are predominantly based on the concept of ``speaker embedding", which transforms variable-length speech signals into fixed-length speaker vectors, followed by verification based on cosine similarity between the embeddings of the enrollment and test utterances. However, this approach suffers from considerable performance degradation in the presence of severe noise and interference speakers. This paper introduces Neural Scoring, a novel framework that re-treats speaker verification as a scoring task using a Transformer-based architecture. The proposed method first extracts an embedding from the enrollment speech and frame-level features from the test speech. A Transformer network then generates a decision score that quantifies the likelihood of the enrolled speaker being present in the test speech. We evaluated Neural Scoring on the VoxCeleb dataset across five test scenarios, comparing it with the state-of-the-art embedding-based approach. While Neural Scoring achieves comparable performance to the state-of-the-art under the benchmark (clean) test condition, it demonstrates a remarkable advantage in the four complex scenarios, achieving an overall 64.53% reduction in equal error rate (EER) compared to the baseline.

Multi-Domain Adaptation by Self-Supervised Learning for Speaker Verification

In real-world applications, speaker recognition models often face various domain-mismatch challenges, leading to a significant drop in performance. Although numerous domain adaptation techniques have been developed to address this issue, almost all present methods focus on a simple configuration where the model is trained in one domain and deployed in another. However, real-world environments are often complex and may contain multiple domains, making the methods designed for one-to-one adaptation suboptimal. In our paper, we propose a self-supervised learning method to tackle this multi-domain adaptation problem. Building upon the basic self-supervised adaptation algorithm, we designed three strategies to make it suitable for multi-domain adaptation: an in-domain negative sampling strategy, a MoCo-like memory bank scheme, and a CORAL-like distribution alignment. We conducted experiments using VoxCeleb2 as the source domain dataset and CN-Celeb1 as the target multi-domain dataset. Our results demonstrate that our method clearly outperforms the basic self-supervised adaptation method, which simply treats the data of CN-Celeb1 as a single domain. Importantly, the improvement is consistent in nearly all in-domain tests and cross-domain tests, demonstrating the effectiveness of our proposed method.