论文标题
神经I-向量
Neural i-vectors
论文作者
论文摘要
在最近的演讲者验证评估中，已经证明了深扬声器的嵌入量优于其生成的I-Vectors。为了结合高性能和生成解释的益处，我们研究了深层嵌入提取器和I-vector提取器的使用。为了将深入嵌入提取器与I-vector提取器捆绑在一起，我们采用受高斯混合模型（GMM）启发的聚合层到嵌入式提取器网络中。包含GMM的层，可以将经过歧视的训练的网络用作I-矢量提取器足够统计的提供商，以提取我们所谓的神经I-Vector。我们将深层嵌入与野外演讲者（SITW）和说话者识别评估（SRE）2018和2019年数据集的拟议的神经I-向量进行比较。在SITW的核心核心条件下，我们的深层嵌入获得了与最先进的性能比较。神经I-向量的性能比深嵌入的性能差约50％，但另一方面，在文献中先前的I-vector方法胜过明确的I-vector方法。
Deep speaker embeddings have been demonstrated to outperform their generative counterparts, i-vectors, in recent speaker verification evaluations. To combine the benefits of high performance and generative interpretation, we investigate the use of deep embedding extractor and i-vector extractor in succession. To bundle the deep embedding extractor with an i-vector extractor, we adopt aggregation layers inspired by the Gaussian mixture model (GMM) to the embedding extractor networks. The inclusion of GMM-like layer allows the discriminatively trained network to be used as a provider of sufficient statistics for the i-vector extractor to extract what we call neural i-vectors. We compare the deep embeddings to the proposed neural i-vectors on the Speakers in the Wild (SITW) and the Speaker Recognition Evaluation (SRE) 2018 and 2019 datasets. On the core-core condition of SITW, our deep embeddings obtain performance comparative to the state-of-the-art. The neural i-vectors obtain about 50% worse performance than the deep embeddings, but on the other hand outperform the previous i-vector approaches reported in the literature by a clear margin.
