Time-Inhomogeneous 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(N^2.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.
For details, see [http://casa.dit.unitn.it/resources/library/icassp08/pdfs/0004101.pdf] or [http://www.ieeexplore.ieee.org/iel5/4505270/4517521/04518556.pdf] .

[1] Jahanshah Kabudian, M. Mehdi Homayounpour, S. Mohammad Ahadi, "Time-inhomogeneous hidden Bernoulli model: An alternative to hidden Markov model for automatic speech recognition," "Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)", pp. 4101-4104, Las Vegas, Nevada, USA, March 2008.