Developing pattern recognition systems based on Markov models: The ESMERALDA framework

• Published in: Pattern recognition and image analysis Vol. 18; no. 2; pp. 207 - 215
• Main Authors: Fink, G. A., Plötz, T.
• Format: Journal Article
• Language: English
• Published: Dordrecht SP MAIK Nauka/Interperiodica 01.06.2008; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Handwriting; Image Processing and Computer Vision; Markov analysis; Pattern Recognition; Pattern recognition systems; Plenary Papers; Random variables; Speech; Stochastic models; Studies; Voice recognition; Speech Recognition; Automatic Speech Recognition; Handwriting Recognition; Markov Chain Model; Hide Markov Model
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S1054661808020041

In this paper we describe ESMERALDA—an integrated Environment for Statistical Model Estimation and Recognition on Arbitrary Linear Data Arrays—which is a framework for building statistical recognizers operating on sequential data as, e.g., speech, handwriting, or biological sequences. ESMERALDA primarily supports continuous density Hidden Markov Models (HMMs) of different topologies and with user-definable internal structure. Furthermore, the framework supports the incorporation of Markov chain models (realized as statistical n -gram models) for long-term sequential restrictions and Gaussian mixture models (GMMs) for general classification tasks. ESMERALDA is used by several academic and industrial institutions. It was successfully applied to a number of challenging recognition problems in the fields of automatic speech recognition, offline handwriting recognition, and protein sequence analysis. The software is open source and can be retrieved under the terms of the LGPL.