A high-performance text-independent speaker identification of Arabic speakers using a CHMM-based approach

This paper reports an approach that depends on Continuous Hidden Markov Models (CHMMs) to identify Arabic speakers automatically from their voices. The Mel-Frequency Cepstral Coefficients (MFCCs) were selected to describe the speech signal. The general Gaussian density distribution HMM is developed for the CHMM system. Ten Arabic speakers were used to evaluate our proposed CHMM-based engine. The identification rate was found to be 100% during text dependent experiments. However, for the text-independent experiments, the identification rate was found to be 80%.