Experiments with a Gaussian Merging-Splitting Algorithm for HMM Training for Speech Recognition
Ananth Sankar · 2007
It is well known that the expectation-maximization (EM) algorithm, commonly used to estimate hidden Markov model (HMM) parameters for speech recognition, is sensitive to the initial model parameter values, making appropriate parameter initialization important. We investigate the use of iterative Gaussian splitting and EM training to initialize the desired number of Gaussians per HMM state (or state cluster). We then study merging of Gaussians which contain little training data as an approach to robust parameter estimation. Finally Gaussian merging and splitting is combined to form the Gaussian Merging-Splitting (GMS) algorithm. Detailed experimental studies show that Gaussian splitting gives similar performance to our previous training algorithm, even though the two algorithms give very different parameter values. The robust parameter estimation from Gaussian merging results in better performance than our old algorithm for speaker-independent models that have a large number of paramete...