Baum-Welch expectation maximization library

Red led Version 0.3.7 is now available for download.

NEW Baum-Welch is now available on conda-forge. Linux (x86) and Mac OS X users who already have conda can install the library and all dependencies using the following command: conda install -c conda-forge baumwelch.

OpenGrm Baum-Welch is a C++ library (including associated binaries) which allows the user to estimate the parameters of a discrete hidden Markov model (HMM) using the Baum-Welch algorithm (a special case of the expectation maximization meta-algorithm). It uses OpenFst library finite-state transducers (FSTs) and FST archives (FARs) as inputs and outputs.

If you use this toolkit in your research, we would appreciate it if you cited at least one of:

K. Gorman and C. Allauzen. 2022. A* shortest string decoding for non-idempotent semirings. arXiv:2204.07236. (Describes the decoding algorithm.)

K. Gorman, C. Kirov, B. Roark, and R. Sproat. 2021. Structured abbreviation expansion in context. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 995-1005. (Describes the pair n-gram formulation.)

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This topic: GRM > WebHome > BaumWelch
Topic revision: r16 - 2022-04-18 - KyleGorman
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