A continuous parameter and frequency domain based Markov model

In most of the existing Automatic Speech Recognition Systems which make use of Markov Models, the output of the Markov Chain are strings whose symbols belong to a finite alphabet and are generated sequentially over the time domain. We propose a Markov Model System in which symbols are substituted by Spectral Lines which are sequentially generated over the frequency domain. Each spectral line is represented by Continuous Distribution of Parameters. Switching from time-domain to frequency domain drastically reduces the number of states on the Markov chain and the use of continuous parameters eliminates quantization error completely. An application will be presented with experimental results in a multi-speaker environment.