There are many available resources on non-parametric HMMs, extensions to infinite state spaces, as well as factorial models, explaining an observation using a set of factors rather than a single mixture component.
Here are some interesting papers to start with:
- 'Beam Sampling for the Infinite Hidden Markov Model'
- 'The Infinite Factorial Hidden Markov Model'
- 'Bayesian Non-parametric Inference of Switching Dynamic Linear Models'
- 'Sharing features among dynamical systems with beta processes'
There are some applications in text modeling, speaker diarization, and motion capture, among other things.
For more details on this, study Types of Machine Learning, which is a part of Machine Learning Tutorial.
Hope this answer helps.