A numpy/python-only Hidden Markov Models framework. No other dependencies are required.
This implementation (like many others) is based on the paper: "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, LR RABINER 1989"
Major supported features:
- Discrete HMMs
- Continuous HMMs - Gaussian Mixtures
- Supports a variable number of features
- Easily extendable with other types of probablistic models (simply override the PDF. Refer to 'GMHMM.py' for more information)
- Non-linear weighing functions - can be useful when working with a time-series
Open concerns:
- Examples are somewhat out-dated
- Convergence isn't guaranteed when using certain weighing functions
Duration-model extension : done by georgi.dzhambazov@upf.edu
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fixed underflow due to multiplication of probabilities ( handled by sum log(probs) )
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posteriors of GMM mixtures are computed using scikit learn
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added oracle test: Oracle test allow to check if model is able to perform perfect alignment on ground truth timestamps of phonemes (e.g. replace observation posteriors with 1-s from ground truth) (described more here: http://www.terasoft.com.tw/conf/ismir2014/proceedings/T050_126_Paper.pdf ) hmm.examples.tests.test_oracle() - needs phoneme-level annotation
Algorithm parameters can be changed from class hmm.ParametersAlgo
TODO: use _ContinuousHMM.usePersistentProbs to store persistently .durationsMap to save time Now computed each time