论文标题
跨语言扬声器验证具有域平衡的硬原型挖掘和语言依赖性分数归一化
Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization
论文作者
论文摘要
在本文中，我们描述了短期扬声器验证（SDSV）挑战2020的文本独立任务的最高得分IDLAB提交。挑战的主要困难是在潜在的交叉语言试验之间发生了不同程度的语音重叠，以及对内域Deepmine Deepmine Farsi培训数据的可用性有限。我们介绍了域均衡的硬原型挖掘，以微调基于X-vector的最先进的ECAPA-TDNN的扬声器嵌入提取器。样本挖掘技术有效利用流行的AAM-SoftMax损耗功能的扬声器原型之间的扬声器距离，以构建在域级别上平衡的挑战训练批次。为了增强跨语性试验的评分，我们提出了依赖语言的S-声称评分归一化。冒名顶替者队列仅包含来自FARSI目标域中的数据，这些数据模拟了入学数据始终是FARSI。如果Gaussian-backend语言模型检测到嵌入英语的测试扬声器，则从最大预期的冒名顶替得分中减去了在AAM-SoftMax扬声器原型上确定的跨语言补偿偏移量。五个具有较小拓扑调整的系统的融合导致最终的MindCF和EER为0.065和1.45％的SDSVC评估集。
In this paper we describe the top-scoring IDLab submission for the text-independent task of the Short-duration Speaker Verification (SdSV) Challenge 2020. The main difficulty of the challenge exists in the large degree of varying phonetic overlap between the potentially cross-lingual trials, along with the limited availability of in-domain DeepMine Farsi training data. We introduce domain-balanced hard prototype mining to fine-tune the state-of-the-art ECAPA-TDNN x-vector based speaker embedding extractor. The sample mining technique efficiently exploits speaker distances between the speaker prototypes of the popular AAM-softmax loss function to construct challenging training batches that are balanced on the domain-level. To enhance the scoring of cross-lingual trials, we propose a language-dependent s-norm score normalization. The imposter cohort only contains data from the Farsi target-domain which simulates the enrollment data always being Farsi. In case a Gaussian-Backend language model detects the test speaker embedding to contain English, a cross-language compensation offset determined on the AAM-softmax speaker prototypes is subtracted from the maximum expected imposter mean score. A fusion of five systems with minor topological tweaks resulted in a final MinDCF and EER of 0.065 and 1.45% respectively on the SdSVC evaluation set.
