Wide-frequency spectral analysis, autoregressive hidden Markov models (ARHMM) and self-organising neural networks (SOM) have
been used for high accuracy speaker features modelling. The initial ARHMM parameters estimation based on Kalman filter is
proposed. The five-keyword speaker identification system has been built and tested. The experiments show that this approach
provides high accuracy of speaker identification even if the same words are pronounced by different speakers.