Automatic Speaker Recognition of Identical Twins

Abstract

Automatic speaker recognition systems typically rely on parameters derived from resonance features of the vocal tract. Th is implies that the more similar the geometry of two vocal tracts is, the more similar will be the respective similarity coeffi cients, or likelihood ratios (LRs). Quite obviously this problem is particularly relevant to related speakers, most extremely for identical (monozygotic) twins. Th is paper is about an experiment with 9 male and 26 female pairs of identical twins who produced one read and one spontaneous speech sample. An automatic system for forensic speaker recognition (Batvox 3.1) was used to calculateinter-speaker (non-target), (2) intratwin pair, and (3) intra-speaker (target) LR distributions. Results show that in certain conditions an automatic Bayesian-based system is capable of distinguishing even the vast majority of very similar sounding voices such as those of identical twins. However, the performance of the system used here was superior for male as compared to female voices. Quite obviously the sex-related diff erence was enhanced by the genetic similarity factor.