Implicit Segmentation in Two-Wire Speaker Recognition

This paper presents a novel self-contained two-wire speaker recognition framework. The classical approach to two-wire speaker recognition usually requires a preliminary explicit speaker segmentation stage in order to extract audio files for the two hypothesized speakers. We propose an implicit speaker segmentation method implemented at the supervector level of speaker recognition systems. By periodically extracting successive supervectors from the two-wire audio it is possible to further associate them to each of the hypothesized speakers before scoring both streams. We show that the proposed technique leads to recognition performance comparable to standard approaches while requiring substantially less resources.