This paper describes a Viterbi-like decoding algorithm applied on segment-models based on linear dynamic systems (LDMs). LDMs are a promising acoustic modeling scheme which can alleviate several of the limitations of the popular Hidden Markov Models (HMMs). There are several implementations of LDMs that can be found in the literature. For our decoding experiments we consider general identifiable forms of LDMs which allow increased state space dimensionality and relax most of the constraints found in other approaches. Results on the AURORA2 database show that our decoding scheme significantly outperforms standard HMMs, particularly under significant noise levels.
Bibliographic reference. Oikonomidis, Dimitris / Diakoloukas, Vassilis / Digalakis, Vassilis (2007): "A sub-optimal viterbi-like search for linear dynamic models classification", In INTERSPEECH-2007, 1717-1720.