In this paper, we present an overview of the RWTH Aachen large vocabulary continuous speech recognizer. The recognizer is based on continuous density hidden Markov models and a time-synchronous left-to-right beam search strategy. Experimental results on the ARPA Wall Street Journal (WSJ) corpus verify the effects of several system components, namely linear discriminant analysis, vocal tract nor...

In this paper, we present an overview of the RWTH Aachen large vocabulary continuous speech recognizer. The recognizer is based on continuous density hidden Markov models and a time-synchronous left-to-right beam search strategy. Experimental results on the ARPA Wall Street Journal (WSJ) corpus verify the effects of several system components, namely linear discriminant analysis, vocal tract nor...

In this work a method for splitting continuous mixture density hidden Markov models (HMM) is presented. The approach combines a model evaluation measure based on the Maximum Mutual Information (MMI) criterion with subsequent standard Maximum Likelihood (ML) training of the HMM parameters. Experiments were performed on the SieTill corpus for telephone line recorded German continuous digit string...

The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM). The tutorial is intended for the practicing engineer, biologist, linguist or programmer who would like to learn more about the above mentioned fascinating mathematical models and include them into one’s repertoire. This part of the tutorial is devoted to the basic concepts of a Hidden Markov Model. You...

Description Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.