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We introduce a new procedure to neuralize unsupervised Hidden Markov Modelsin the continuous case. This provides higher flexibility to solve problems withunderlying latent variables. This approach is evaluated on both synthetic andreal data. On top of generating likely model parameters with comparableperformances to off-the-shelf neural architecture (LSTMs, GRUs,..), theobtained results are easily interpretable.

Unsupervised Neural Hidden Markov Models with a Continuous latent state space