1 Hidden Markov Models with Finite State Supervision Eric

Relative Entropy Rate between a Markov Chain and Its Corresponding Hidden Markov Chain

 In this paper we study the relative entropy rate between a homogeneous Markov chain and a hidden Markov chain defined by observing the output of a discrete stochastic channel whose input is the finite state space homogeneous stationary Markov chain. For this purpose, we obtain the relative entropy between two finite subsequences of above mentioned chains with the help of the definition of...

Modeling Time Series With Auto-Regressive Markov Models

It reviews the theory of Hidden Filter Hidden Markov Models and presents an extension, Mixed State Hidden Markov Models, developed jointly by Andrew Fraser and myself under his supervision. This manuscript version has only trivial differences from the original.

A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model

A formulation of the maximum a posteriori (MAP) approach to speaker adaptation is presented with use of the trended or nonstationary-state hidden Markov model (HMM), where the Gaussian means in each HMM state are characterized by time-varying polynomial trend functions of the state sojourn time. Assuming uncorrelatedness among the polynomial coefficients in the trend functions, we have obtained...

Improved topic classification and keyword discovery using an HMM-based speech recognizer trained without supervision

In our previous publication [1], we presented a new approach to HMM training, viz., training without supervision. We used an HMM trained without supervision for transcribing audio into self-organized units (SOUs) for the purpose of topic classification. In this paper we report improvements made to the system, including the use of context dependent acoustic models and lattice based features that...

Hidden Markov Models and other Finite State Automata for Sequence Processing