Low-Complexity Automatic Speaker Recognition in the Compressed GSM-AMR Domain

This paper presents an experimental implementation of a low-complexity speaker recognition algorithm working in the compressed speech domain. The goal is to perform speaker modeling and identication without decoding the speech bitstream to extract speaker dependent features, thus saving important system resources, for instance, in mobile devices. The compressed bitstream values of the widely used GSM AMR speech coding standard are studied to identify statistics enabling fair recognition after a few seconds of speech. Using euclidean distance measures on elementary statistical values such as coefficient of variation and skewness of nine standard GSM AMR parameters delivers recognition accuracies close to 100% after about 20 seconds of active speech for a database of 14 speakers recorded in a normal room environment.