Interval calculation of EM algorithm for GMM parameter estimation

This work proposes a low complexity computation of EM algorithm for Gaussian mixture model(GMM) and accelerates the parameter estimation. In previous works, the authors revealed that the computational complexity of GMM-based classification can be reduced by using an interval calculation technique. This work applies the idea to EM algorithm for GMM parameter estimation. From experiments, it is confirmed that the computational speed of the proposal achieves more than twice that of the standard method with ´exp( )´ function. The relative errors are less than 0.6% and 0.053% when the number of bits for table addressing are 4 and 8, respectively.