 In the same way it can be beneficial to embed the HMM in a layered structure which, theoretically, may not be able to solve any problems the basic HMM cannot, but can solve some problems more efficiently because less training data is needed.A layered hidden Markov model (LHMM) consists of formula_1 levels of HMMs where the HMMs on level formula_2 corresponds to observation symbols or probability generators at level formula_1.Every level formula_4 of the LHMM consists of formula_5 HMMs running in parallel.