Influence of initialisation and stop criteria on HMM based recognisers

A study is presented into the importance of two commonly overlooked factors influencing generalisation ability in the field of hidden Markov model (HMM) based recogniser training algorithms by means of a comparative study of four initialisation methods and three stop criteria in different applications. The results show that better results have been found with the equal-occupancy initialisation method and the fixed-threshold stop criterion.