Under this topic you can find information about the Kaldi ASR Toolkit, like URLs and paths where to find it. Kaldi is a more recent ASR toolkit compared to SPRAAK. Like SPRAAK, it contains functionality to train different types of GMM-HMM acoustic models, but also various types of Deep Neural Networks (DNNs), the current standard in ASR. This page provides links to Kaldi's own documentation pages and tips & tricks on how to use Kaldi for certain contexts. Feel free to add experiences which you feel are useful to others (i.e. to not 'reinvent the wheel').

Name: Kaldi ASR
Type: open source software
Developer info page: http://www.kaldi-asr.org
Documentation page: http://www.kaldi-asr.org/doc/
Compile and installation instructions: http://kaldi-asr.org/doc/install.html

Location of Kaldi sources: https://github.com/kaldi-asr/kaldi
Location of linux x86_64 compiled binaries (usable on Ponyland): applejack.science.ru.nl:/vol/tensusers/mganzeboom/software/kaldi-master. These are compiled with NVIDIA CUDA 7.5 GPU support. In other words NVIDIA CUDA 7.5 should be installed on the target machine (already installed on Ponyland).
Note: for the above applejack urls a Ponyland SSH account is required from Wessel Stoop

There are some useful tutorials to get to know the Kaldi toolkit and how to operate it to do various things. Below a list sorted per topic.
Training acoustic models

• Kaldi's own relatively detailed tutorial providing an overview of the toolkit: http://kaldi-asr.org/doc/tutorial.html
• Kaldi for Dummies tutorial for absolute beginners of Kaldi: http://kaldi-asr.org/doc/kaldi_for_dummies.html
• Eleanor Chodroff's tutorial: http://pages.jh.edu/~echodro1/tutorial/kaldi/kaldi-intro.html

Forced alignment using existing acoustic models

• Eleanor Chodroff's page: http://pages.jh.edu/~echodro1/tutorial/kaldi/kaldi-forcedalignment.html