We propose a method to approximate the marginal likelihood of hidden Markov models with intractable probability functions. The new approach is based on the reciprocal importance sampling combined with the exchange algorithm. The marginal likelihood can be approximated from the output of Markov Chain Monte Carlo algorithms, involving only the unnormalized posterior densities from the sampled parameter values, and does not involve additional simulations beyond the main posterior simulation.