arXiv:2605.29208v1 Announce Type: cross Abstract: We describe libhmm, a C++20 library for Hidden Markov Model parameter estimation, sequence decoding, and model selection. libhmm addresses two gaps in existing software: the absence of a well-maintained, zero-dependency C++ HMM library suitable for embedding in production systems, and the widespread use of method-of-moments (MOM) approximations in the emission distribution M-step of the Baum-Welch algorithm. The library implements correct maximum likelihood estimators for sixteen continuous and discrete emission distributions, including an ECME
The increasing complexity and demand for reliable AI systems across various domains drives the need for more robust and accurate foundational libraries, particularly given past approximations.
This development addresses a critical technical gap in Hidden Markov Model implementation, improving the accuracy and production readiness of systems reliant on HMMs, thus impacting fields from speech recognition to bioinformatics.
The availability of a C++20, zero-dependency HMM library with correct maximum likelihood estimators for a wide array of emission distributions lowers the barrier for developers building high-performance, robust HMM-based applications.
- · Developers leveraging HMMs
- · Sectors using HMMs (e.g., speech, finance, biology)
- · C++ ecosystem
- · Research in statistical modeling
- · Software using imprecise HMM implementations
- · Proprietary HMM libraries with less rigor
More accurate and reliable HMM-based models will be deployed in production systems, reducing error rates.
Improved HMM foundational tools may spur innovation in applications where HMM accuracy was previously a limiting factor.
The open-source nature and correctness criteria could set new benchmarks for statistical library development across other machine learning algorithms.
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