{{third-party, date=April 2013 Time-inhomogeneous hidden Bernoulli model (TI-HBM) is an alternative to

hidden Markov model A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an ob ...

(HMM) for

automatic speech recognition Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the ma ...

. 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 Dynamic programming is both a mathematical optimization method and a computer programming method. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics. I ...

at state-level in TI-HBM decoding process. Thus, the computational complexity of TI-HBM for probability evaluation and state estimation is

O(NL)

(instead of

O(N^2L)

in the HMM case, where

N

and

L

are number of states and observation 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, se

o

References

* Jahanshah Kabudian, M. Mehdi Homayounpour, S. Mohammad Ahadi, "Bernoulli versus Markov: Investigation of state transition regime in switching-state acoustic models," ''Signal Processing'', vol. 89, no. 4, pp. 662–668, April 2009. * 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 the IEEE

International Conference on Acoustics, Speech and Signal Processing ICASSP, the International Conference on Acoustics, Speech, and Signal Processing, is an annual flagship conference organized of IEEE Signal Processing Society. All papers included in its proceedings have been indexed by Ei Compendex. The first ICAS ...

(ICASSP)'', pp. 4101–4104, Las Vegas, Nevada, USA, March 2008. Speech recognition Hidden stochastic models