Performances of unsupervised hmm in acoustic-to-articulatory inversion

In the context of the acoustic-to-articulatory inversion, various unsupervised HMM based feature-mapping methods are assessed and compared. In a previous study we introduced an unsupervised HMM as an alternative model to the phone-HMM. We propose here to evaluate this approach using different inversion methods, in order to assess the behavior of our model and its compatibility with the most efficient inversion algorithms available. The best configuration leads to similar root mean square error (up to 1.44 mm) than phoneme-based HMM.