Abstract
Total variability model-based i-vector and deep neural network-based embedding x-vector are both widely used for text-independent speaker verification. In this Letter, a novel model is proposed, which can contain information of both i-vector and x-vector by using parallel factor analysis. The authors aim to obtain a linear transformation expression for x-vectors based on background i-vectors and x-vectors, and consider the linearly transformed x-vector as the novel model, thus they name it as -vector. The novel -vector can maximise intra and minimise inner speaker variability, in addition, it can improve the system performance without latency. Experiments were conducted on NIST 2010 dataset, and in terms of equal error rate, they observe up to 37.27 and 53.38% relative improvement of the authors proposed -vector model compared to the i-vector and x-vector models, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
