  We introduce a new procedure to neuralize unsupervised Hidden Markov Models
in the continuous case. This provides higher flexibility to solve problems with
underlying latent variables. This approach is evaluated on both synthetic and
real data. On top of generating likely model parameters with comparable
performances to off-the-shelf neural architecture (LSTMs, GRUs,..), the
obtained results are easily interpretable.
