Generative X-vectors For Text-independent Speaker Verification

Abstract

Speaker verification (SV) systems using deep neural network embeddings, so-called the x-vector systems, are becoming popular due to its good performance superior to the i-vector systems. The fusion of these systems provides improved performance benefiting both from the discriminatively trained x-vectors and generative i-vectors capturing distinct speaker characteristics. In this paper, we propose a novel method to include the complementary information of i-vector and x-vector, that is called generative x-vector. The generative x-vector utilizes a transformation model learned from the i-vector and x-vector representations of the background data. Canonical correlation analysis is applied to derive this transformation model, which is later used to transform the standard x-vectors of the enrollment and test segments to the corresponding generative x-vectors. The SV experiments performed on the NIST SRE 2010 dataset demonstrate that the system using generative x-vectors provides considerabl

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keyxu2018generative

Related papers

• I-vector Transformation Using Conditional Generative Adversarial Networks For Short Utterance Speaker Verification (2018)8.35
• Discriminatively Re-trained I-vector Extractor For Speaker Recognition (2018)5.84
• Gaussian Speaker Embedding Learning For Text-independent Speaker Verification (2020)0.00
• Probing The Information Encoded In X-vectors (2019)13.23
• Generative Adversarial Speaker Embedding Networks For Domain Robust End-to-end Speaker Verification (2018)0.00
• Multi-task Learning With High-order Statistics For X-vector Based Text-independent Speaker Verification (2019)8.35
• Bayesian X-vector: Bayesian Neural Network Based X-vector System For Speaker Verification (2020)6.77
• Adversarial Attacks On GMM I-vector Based Speaker Verification Systems (2019)13.65