Estimation of general identifiable linear dynamic models with an application in speech recognitionEstimation of general identifiable linear dynamic models with an application in speech recognition Δημοσίευση σε Συνέδριο Conference Publication 2015-11-082007enAlthough hidden Markov models (HMMs) provide a relatively efficient modeling framework for speech recognition, they suffer from several shortcomings which set upper bounds in the performance that can be achieved. Alternatively, linear dynamic models (LDM) can be used to model speech segments. Several implementations of LDM have been proposed in the literature. However, all had a restricted structure to satisfy identifiability constraints. In this paper, we relax all these constraints and use a general, canonical form for a linear state-space system that guarantees identifiability for arbitrary state and observation vector dimensions. For this system, we present a novel, element-wise maximum likelihood (ML) estimation method. Classification experiments on the AURORA2 speech database show performance gains compared to HMMs, particularly on highly noisy conditions. http://creativecommons.org/licenses/by/4.0/IEEE International Conference on Acoustics, Speech and Signal Processing Tsontzos Georgios Diakoloukas Vasilis Διακολουκας Βασιλeioς Koniaris, Christos, 1979- Digalakis Vasilis Διγαλακης Βασιλης Institute of Electrical and Electronics Engineers HMMs (Hidden Markov models) hidden markov models hmms hidden markov models Mechanical speech recognizer Speech recognition, Automatic automatic speech recognition mechanical speech recognizer speech recognition automatic