TU Dortmund Department of Computer Science LS XII Pattern Recognition GroupPublications → Publication Details

ESMERALDA: A Development Environment for HMM-Based Pattern Recognition Systems

G. A. Fink and T. Pl{\"o}tz
Proc. 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding (OGRW 2007), Ettlingen, Germany, 2007.

In this paper we describe ESMERALDA - an integrated Environment for Statistical Model Estimation and Recognition on Arbitrary Linear Data Arrays - which is a development toolkit 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. In recent years various applications within different challenging research areas have been realized using ESMERALDA.

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