Nonparametric Bayesian Double Articulation Analyzer
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.
- Ubuntu 16.04 LTS
- Python 3.6.5
- Numpy 1.14.2
- Scipy 1.0.1
- Scikit-learn 0.19.1
- Matplotlib 2.2.2
- Joblib 0.11
- Cython 0.28.2
- tqdm 4.23.4
- pybasicbayes 0.2.2
- pyhsmm 0.1.6
- Install GNU compiler collection to use Cython.
$ sudo apt install gcc
- Install the necessary libraries for installation.
$ pip install numpy future six $ pip install cython
- Install pybasicbayes.
$ git clone https://github.com/mattjj/pybasicbayes $ cd pybasicbayes $ python setup.py install
- Install pyhsmm.
$ git clone https://github.com/RyoOzaki/pyhsmm $ cd pyhsmm $ python setup.py install
The repository of pyhsmm was forked and updated by Ryo Ozaki. If you want to install pyhsmm of master repository, please go to https://github.com/mattjj/pyhsmm But, the master repository's codes include some bugs in cython codes.
- Install pyhlm (this).
$ git clone https://github.com/EmergentSystemLabStudent/NPB_DAA npbdaa $ cd npbdaa $ python setup.py install
There is a sample source of NPB-DAA in "sample" directory. Please run the "unroll_default_config" before run "pyhlm_sample", and you can change the hyperparameters using the config file "hypparams/defaults.config".
$ cd sample $ python unroll_default_config.py $ python pyhlm_sample.py $ python summary_and_plot.py
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, and Ryo Ozaki.
- see LICENSE