Hidden Bernoulli Model

Time-Inhomogeneous Hidden Bernoulli Model (TI-HBM) is an alternative to Hidden Markov Model (HMM) for Automatic Speech Recognition. Contrary to HMM, the state transition process in TI-HBM is not a Markov-dependent process, rather it is a generalized Bernoulli (an independent) process. This difference leads to elimination of dynamic programming at state-level in TI-HBM decoding process. Thus, the computational complexity of TI-HBM for Probability Evaluation and State Estimation is O(N.L) (instead of O(N2.L) in the HMM case, where N and L are number of states and sequence length respectively). The TI-HBM is able to model acoustic-unit duration (e.g. phone/word duration) by using a built-in parameter named survival probability. The TI-HBM is simpler and faster than HMM in a phoneme recognition task, but its performance is comparable to HMM.