A real-time trained system for robust speaker verification using relative space of anchor models

A real-time trained system for robust speaker verification is proposed. This system was developed using a relative space of reference speakers, also referred to as anchor models. The real-time training aspect of the system is based on this relative space’s intriguing features and properties. The relative space concept uses relative speaker representation rather than an absolute representation, by comparing the speaker to a set of well-trained reference speakers. The advantage of this approach is that instead of estimating numerous parameters of an absolute model for a speaker, only a few parameters of a model relative to a number of anchor models are estimated. In order to optimize the performance of the proposed system, several techniques were assessed for possible implementation in various blocks of the system. As a result, the best performance was achieved where normalized vector’s mutual angle with the Minimum normalization method was applied to speaker verification in conjunction with an orthogonal relative space of virtual reference speakers. In this case, an Equal Error Rate (EER) of 0.12% on 400 test samples of 100 speakers was obtained. In addition to assessment under normal conditions, the developed speaker verification system was also evaluated under abnormal conditions where noisy or telephonic speech sequence contamination was present. Experiments conducted in this case demonstrated that, in most cases, this system outperforms absolute space based systems even with shortened training speech sequences. Another major contribution of this research is the development of a more complex speaker verification system capable of tackling abnormal conditions more effectively. In this case, other interesting features of the relative space approach were employed. For this purpose, a novel enrichment method was developed to construct a relative space of anchor models trained to tackle noise. The results of the experiments conducted in this part of the research demonstrated an excellent ability of this approach to tackle abnormal conditions. Compared to absolute space based system, applying this method in relative space led to lower error rates of speaker verification in all cases even with low SNR values.