This is a Python implementation for Nonparametric Bayesian Double Articulation Analyzer (NPB-DAA). The NPB-DAA can directly acquire language and acoustic models from observed continuous speech signals.
This generative model is called hiererichel Dirichlet process hidden language model (HDP-HLM), which is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM.
・NPB_DAA/README - There is a NPB-DAA tutorial in PDF.(In Japanese. English version is coming soon.)
・NPB_DAA/pyhsmm - Python Library for HDP-HSMM. You can get it at [ https://github.com/mattjj/pyhsmm ]. (Please check this VERSION at README)
・NPB_DAA/dahsmm - Python code for NPB-DAA
・Ubuntu 12.04.5 LTS
sudo apt-get install
・python 2.7.3
・numpy 1.6.1
・matplotlib 1.1.1rc
・scipy 0.9.0
・scikit-learn 0.10
sudo pip install
・Paver 1.2.4
・pyzmq==14.4.0/14.5.0/14.6.0 (14.6.0)
・ipython 3.2.1
Tutroial is put in NPB_DAA/README. Please read it. (In Japanese. English version is coming soon.)
If you are in trouble, please look at this document. You can get information about environment operability confirmed and actions on error occuring often. Troubleshooting document
・Taniguchi, Tadahiro, Shogo Nagasaka, and Ryo Nakashima. Nonparametric Bayesian double articulation analyzer for direct language acquisition from continuous speech signals, 2015.
・Matthew J. Johnson and Alan S. Willsky. Bayesian Nonparametric Hidden Semi-Markov Models. Journal of Machine Learning Research (JMLR), 14:673–701, 2013.
Tadahiro Taniguch, Ryo Nakashima, Nagasaka Shogo, Tada Yuki, Kaede Hayashi.
- MIT
- see LICENSE