提出神经评分框架，直接输出说话人验证概率，提升复杂场景下的识别鲁棒性。
Neural Scoring: A Refreshed End-to-End Approach for Speaker Recognition in Complex Conditions
- 直接端到端预测验证后验概率，无需测试侧嵌入向量
- 在VoxCeleb上相较基线降低70.36%的EER
- 适用于多说话人等复杂场景，适合实际部署
现代说话人验证系统主要依赖说话人嵌入向量，并通过计算注册语音与测试语音嵌入向量之间的余弦相似度进行验证。尽管有效，这些方法在多人语音场景下表现不佳，因嵌入向量难以区分。本文提出神经评分（Neural Scoring, NS），一种全新的端到端框架，直接估计验证后验概率，无需依赖测试侧嵌入，从而增强对复杂条件（如多说话人）的鲁棒性。为提高训练效率，引入大规模试验端到端训练（LtE2E）策略，使每个测试语音可与一组注册说话人配对，实现每批次处理大规模验证试验。在VoxCeleb数据集上的实验表明，NS在各种条件下均显著优于基线及竞争方法，整体EER相比基线降低70.36%。
原文摘要 · Abstract (English)
Modern speaker verification systems primarily rely on speaker embeddings, followed by verification based on cosine similarity between the embedding vectors of the enrollment and test utterances. While effective, these methods struggle with multi-talker speech due to the unidentifiability of embedding vectors. In this paper, we propose Neural Scoring (NS), a refreshed end-to-end framework that directly estimates verification posterior probabilities without relying on test-side embeddings, making it more robust to complex conditions, e.g., with multiple talkers. To make the training of such an end-to-end model more efficient, we introduce a large-scale trial e2e training (LtE2E) strategy, where each test utterance pairs with a set of enrolled speakers, thus enabling the processing of large-scale verification trials per batch. Experiments on the VoxCeleb dataset demonstrate that NS consistently outperforms both the baseline and competitive methods across various conditions, achieving an overall 70.36% reduction in EER compared to the baseline.
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