Implementing KPCA-based speaker adaptation methods with different optimization algorithms in a Persian ASR system

In this paper, two kernel eigenspace-based speaker adaptation methods are implemented using FARSDAT database and their performances are compared with eigenspace-based ones. In the conducted experiments, short lengths of adaptation speech data (2-5 seconds) are used. Experimental results show that 4.5% improvement in phoneme recognition rate is achieved by supervised eigenspace-based methods. Implementing kernel eigenspace-based methods, 0.6% improves the results gained by utilizing eigenspace-based methods in 2 seconds of adaptation data. While, with this amount of data, traditional speaker adaptation methods cannot work efficiently. In addition, in this work, we employ another optimization algorithm instead of usual numerical methods, which is particle swarm optimization (PSO) and its performance in achieving rapid optimization is investigated.