Speaker recognition based on multilevel speech signal analysis on Polish corpus
This article deals with a new approach to the text-independent speaker verification task. It is namely proposed to combine spectral and the so-called high-level features (prosodic, articulatory, and lexical) in order to increase accuracy of speaker verification. The presented experiments were performed using a Polish language corpus developed by the authors, the so-called PUEPS corpus. It contains semi-spontaneous telephone conversations (acted emergency telephone notifications) recorded in laboratory conditions. As the Polish language is under resourced and the PUEPS corpus is relatively small, in this case a new approach is needed, other than these well known from NIST (National Institute of Standards and Technology) evaluations. The authors proposed to use the fast scoring instead of more complex classifiers and the AdaBoost (adaptive boosting) algorithm for features combination. Combination of features resulted in the equal error rate (EER) reduction for various SNR (signal-to-noise ratio) conditions. Additionally, score normalization methods were evaluated. It was shown that significant benefits can be obtained using the z-norm2 method.
speaker recognition high-level features boosting kernel combination