DocumentCode :
2793472
Title :
Discriminative HMMS, log-linear models, and CRFS: What is the difference?
Author :
Heigold, G. ; Wiesler, S. ; Nussbaum-Thom, Markus ; Lehnen, P. ; Schluter, R. ; Ney, H.
Author_Institution :
Comput. Sci. Dept., RWTH Aachen Univ., Aachen, Germany
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
5546
Lastpage :
5549
Abstract :
Recently, there have been many papers studying discriminative acoustic modeling techniques like conditional random fields or discriminative training of conventional Gaussian HMMs. This paper will give an overview of the recent work and progress. We will strictly distinguish between the type of acoustic models on the one hand and the training criterion on the other hand. We will address two issues in more detail: the relation between conventional Gaussian HMMs and conditional random fields and the advantages of formulating the training criterion as a convex optimization problem. Experimental results for various speech tasks will be presented to carefully evaluate the different concepts and approaches, including both a digit string and large vocabulary continuous speech recognition tasks.
Keywords :
Gaussian processes; convex programming; hidden Markov models; speech recognition; Gaussian HMM; conditional random fields; convex optimization problem; digit string; discriminative acoustic modeling techniques; hidden Markov models; log-linear models; vocabulary continuous speech recognition tasks; Acoustic emission; Computer science; Constraint optimization; Hidden Markov models; Humans; Paper technology; Pattern recognition; Speech analysis; Speech recognition; Vocabulary; conditional random field; discriminative training; hidden Markov model; log-linear model; speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495228
Filename :
5495228
Link To Document :
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