Editing Speech processing (section)

=== Hidden Markov models ===
{{Main|Hidden Markov model}}A hidden Markov model can be represented as the simplest [[dynamic Bayesian network]]. The goal of the algorithm is to estimate a hidden variable x(t) given a list of observations y(t). By applying the [[Markov property]], the [[conditional probability distribution]] of the hidden variable ''x''(''t'') at time ''t'', given the values of the hidden variable ''x'' at all times, depends ''only'' on the value of the hidden variable ''x''(''t'' − 1). Similarly, the value of the observed variable ''y''(''t'') only depends on the value of the hidden variable ''x''(''t'') (both at time ''t'').{{citation needed|date=December 2018}}